Purpose:In recent decades,with the availability of large-scale scientific corpus datasets,difference-in-difference(DID)is increasingly used in the science of science and bibliometrics studies.DID method outputs the un...Purpose:In recent decades,with the availability of large-scale scientific corpus datasets,difference-in-difference(DID)is increasingly used in the science of science and bibliometrics studies.DID method outputs the unbiased estimation on condition that several hypotheses hold,especially the common trend assumption.In this paper,we gave a systematic demonstration of DID in the science of science,and the potential ways to improve the accuracy of DID method.Design/methodology/approach:At first,we reviewed the statistical assumptions,the model specification,and the application procedures of DID method.Second,to improve the necessary assumptions before conducting DID regression and the accuracy of estimation,we introduced some matching techniques serving as the pre-selecting step for DID design by matching control individuals who are equivalent to those treated ones on observational variables before the intervention.Lastly,we performed a case study to estimate the effects of prizewinning on the scientific performance of Nobel laureates,by comparing the yearly citation impact after the prizewinning year between Nobel laureates and their prizewinning-work coauthors.Findings:We introduced the procedures to conduct a DID estimation and demonstrated the effectiveness to use matching method to improve the results.As a case study,we found that there are no significant increases in citations for Nobel laureates compared to their prizewinning coauthors.Research limitations:This study ignored the rigorous mathematical deduction parts of DID,while focused on the practical parts.Practical implications:This work gives experimental practice and potential guidelines to use DID method in science of science and bibliometrics studies.Originality/value:This study gains insights into the usage of econometric tools in science of science.展开更多
In this study,we propose a Gram-Charlier expansion approach to investigate the impact of skewness and kurtosis on production and hedging decisions.Consistent with the existing literature,we find that skewness and kurt...In this study,we propose a Gram-Charlier expansion approach to investigate the impact of skewness and kurtosis on production and hedging decisions.Consistent with the existing literature,we find that skewness and kurtosis do not affect decisions regarding optimal production;however,they significantly influence optimal hedging decisions.We observe that positive skewness with platykurtic spot prices or negative skewness with leptokurtic spot prices often leads to over-hedging when the initial forward contract price exceeds its expected value.Conversely,under-hedging is expected when the initial forward contract price falls below its expected value.In other conditions,skewness can either promote or impede speculative future trading.Using the Gram-Charlier expansion of the spot price density function,we find that optimal future positions depend on forward prices,the hedgers’risk preference,and the spot price distribution.Simulations validate our findings on the impact of skewness and kurtosis on future hedging.Finally,we analyze of a cotton storage and forward contracting dataset to illustrate the application of our methodology and support our theoretical results.展开更多
The integration of artificial intelligence(AI)into various sectors has undoubtedly brought about numerous benefits,from increased efficiency to innovative problem‐solving.The growing influence of AI across several in...The integration of artificial intelligence(AI)into various sectors has undoubtedly brought about numerous benefits,from increased efficiency to innovative problem‐solving.The growing influence of AI across several industries may help to achieve the sustainable development goals(SDGs).However,due to the AI revolution happening in industries across the globe,older employees are often confronted with significant hurdles in keeping pace with these changes.The threat of job displacement looms large as automation driven by AI encroaches upon routine tasks previously performed by human workers.Job insecurity,that is,worry of losing one's job encompasses anxiety,and uneasiness,and affects the mental health of employees.To address these challenges and empower older employees in the era of open AI,it is imperative that organizations implement targeted strategies tailored to their unique needs and circumstances.Employees use the opportunities for continued education provided to them with company support to prevent unwanted effects.organizations can create an inclusive and supportive environment where older employees are empowered to embrace the opportunities presented by AI while leveraging their experience and expertise to drive innovation and success.展开更多
Remote sensing plays a pivotal role in forest inventory by enabling efficient large-scale monitoring while minimizing fieldwork costs.However,missing values pose a critical challenge in remote sensing applications,as ...Remote sensing plays a pivotal role in forest inventory by enabling efficient large-scale monitoring while minimizing fieldwork costs.However,missing values pose a critical challenge in remote sensing applications,as ignoring or mishandling such data gaps can introduce systematic bias into the estimation of target variables for natural resource monitoring.This can lead to cascading errors that propagate through forest and ecosystem management decisions,ultimately hindering progress toward sustainable forest management,biodiversity conservation,and climate change mitigation strategies.This study aims to propose and demonstrate a procedure that employs hybrid estimators to address the limitations of missing remotely sensed data in forest inventory,using Landsat 7 ETM+SLC-off data as an archived source for forest resource monitoring as a case in point.We compared forest inventory estimates from the hybrid estimator with those from a conventional model-based(CMB)estimator using Sentinel-2 data without missing values.Monte Carlo simulations revealed three key findings:(1)The hybrid estimator,leveraging missing-data remote sensing represented by Landsat 7 ETM+SLCoff data,achieved a sampling precision of over 90%,meeting China's national standard for the National Forest Inventory(NFI);(2)The hybrid estimator demonstrated comparable efficiency to the CMB estimator;(3)The uncertainty associated with hybrid estimators was primarily dominated by model parameter estimation,which could be effectively mitigated by slightly increasing the training sample size or refining model specification.Overall,in forest inventory,the hybrid estimator can surmount the limitations posed by missing values in remotely sensed auxiliary data,effectively balancing cost-effectiveness and flexibility.展开更多
A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment ...A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment effect and interim result.For hypotheses and reversed hypotheses under normal models,we obtain analytical expressions of the ROC curves of the CCP,find optimal ROC curves of the CCP,investigate the superiority of the ROC curves of the CCP,calculate critical values of the False Positive Rate(FPR),True Positive Rate(TPR),and cutoff of the optimal CCP,and give go/no go decisions at the interim of the optimal CCP.In addition,extensive numerical experiments are carried out to exemplify our theoretical results.Finally,a real data example is performed to illustrate the go/no go decisions of the optimal CCP.展开更多
Purpose:This study investigates the impact of domestic mobility on Chinese scientists’academic performance and explores the predictors influencing their chances of moving to more prestigious institutions.Design/metho...Purpose:This study investigates the impact of domestic mobility on Chinese scientists’academic performance and explores the predictors influencing their chances of moving to more prestigious institutions.Design/methodology/approach:Using publication and affiliation data from OpenAlex,we identified 2,896 scientists who relocated between cities in China from 2014 to 2017.We applied propensity score matching(PSM)to compare their academic outcomes post-mobility with a matched group of non-mobile peers.Multiple performance metrics were examined,including publication count,citation impact,number of collaborators,and university prestige.Ordered logistic regression was used to analyze factors influencing moves to higher-level institutions.Findings:Mobility enhances collaboration by increasing the number of coauthors but is associated with a short-term decline in citation impact.Scientists were more likely to move to lower-prestige universities.However,prior collaboration breadth and citation count positively predicted transitions to more prestigious institutions,while the number of publications did not.Research limitations:This study focuses on intra-national mobility within China from 2014 to 2017 and relies on quantitative data,lacking personal or qualitative variables such as gender,discipline-specific norms,or institutional culture.Data coverage for Chinese-language publications may also be limited.Practical implications:This research provides insights into academic hiring patterns and the trade-offs involved in scientist mobility.It offers valuable guidance for institutions aiming to enhance faculty recruitment and retention,as well as for researchers considering career transitions.Originality/value:This is a quantitative analysis of domestic scientist mobility in China using matched comparison and multi-dimensional academic indicators.The integration of university prestige metrics(Double First-Class and citation-based rankings)offers a nuanced view of career dynamics within the Chinese higher education system.展开更多
Purpose:This study investigates factors associated with scientific recognition,examining how collaboration networks influence the path to ACM fellowship.Design/methodology/approach:We analyzed 1,497 ACM fellows(1994-2...Purpose:This study investigates factors associated with scientific recognition,examining how collaboration networks influence the path to ACM fellowship.Design/methodology/approach:We analyzed 1,497 ACM fellows(1994-2023)using linear regression on 286,791 publication records,examining co-authorship patterns and institutional overlaps while controlling for productivity metrics.Findings:Collaboration with ACM fellows among new electees increased from 43%to over 90%.Collaborating with ACM fellows is associated with achieving fellowship 3.8 years earlier,with frequent,recent collaborations and prestigious collaborators exhibiting even shorter time intervals to recognition.Gender and institutional factors also significantly impact timing.Research limitations:The study is correlational,focuses on one society,and may not capture all forms of scientific contribution beyond traditional metrics.Practical implications:Current processes may favor well-connected candidates.Reforms should increase transparency and expand recognition criteria to address biases and promote inclusivity.Originality/value:This provides the first comprehensive three-decade analysis of ACM fellowship patterns,revealing the growing importance of strategic networking in scientific recognition and offering evidencebased recommendations for more inclusive evaluation processes.展开更多
BACKGROUND Pancreatic ductal adenocarcinoma(PDAC)is a serious disease with a poor prognosis.Only a minority of patients undergo surgery due to the advanced stage of the disease,and patients with early-stage disease,wh...BACKGROUND Pancreatic ductal adenocarcinoma(PDAC)is a serious disease with a poor prognosis.Only a minority of patients undergo surgery due to the advanced stage of the disease,and patients with early-stage disease,who are expected to have a better prognosis,often experience recurrence.Thus,it is important to identify the risk factors for early recurrence and to develop an adequate treatment plan.AIM To evaluate the predictive factors associated with the early recurrence of earlystage PDAC.METHODS This study enrolled 407 patients with stage I PDAC undergoing upfront surgical resection between January 2000 and April 2016.Early recurrence was defined as a diagnosis of recurrence within 6 mo of surgery.The optimal cutoff values were determined by receiver operating characteristic(ROC)analyses.Univariate and multivariate analyses were performed to identify the risk factors for early recurrence.RESULTS Of the 407 patients,98 patients(24.1%)experienced early disease recurrence:26(26.5%)local and 72(73.5%)distant sites.In total,253(62.2%)patients received adjuvant chemotherapy.On ROC curve analysis,the optimal cutoff values for early recurrence were 70 U/mL and 2.85 cm for carbohydrate antigen 19-9(CA 19-9)levels and tumor size,respectively.Of the 181 patients with CA 19-9 level>70 U/mL,59(32.6%)had early recurrence,compared to 39(17.4%)of 226 patients with CA 19-9 level≤70 U/mL(P<0.001).Multivariate analysis revealed that CA 19-9 level>70 U/mL(P=0.006),tumor size>2.85 cm(P=0.004),poor differentiation(P=0.008),and non-adjuvant chemotherapy(P=0.025)were significant risk factors for early recurrence in early-stage PDAC.CONCLUSION Elevated CA 19-9 level(cutoff value>70 U/mL)can be a reliable predictive factor for early recurrence in early-stage PDAC.As adjuvant chemotherapy can prevent early recurrence,it should be recommended for patients susceptible to early recurrence.展开更多
Background Somatic symptom disorder(SSD)commonly presents in general hospital settings,posing challenges for healthcare professionals lacking specialised psychiatric training.The Neuro-11 Neurosis Scale(Neuro-11)offer...Background Somatic symptom disorder(SSD)commonly presents in general hospital settings,posing challenges for healthcare professionals lacking specialised psychiatric training.The Neuro-11 Neurosis Scale(Neuro-11)offers promise in screening and evaluating psychosomatic symptoms,comprising 11 concise items across three dimensions:somatic symptoms,negative emotions and adverse events.Prior research has validated the scale’s reliability,validity and theoretical framework in somatoform disorders,indicating its potential as a valuable tool for SSD screening in general hospitals.Aims This study aimed to establish the reliability,validity and threshold of the Neuro-11 by comparing it with standard questionnaires commonly used in general hospitals for assessing SSD.Through this comparative analysis,we aimed to validate the effectiveness and precision of the Neuro-11,enhancing its utility in clinical settings.Methods Between November 2020 and December 2021,data were collected from 731 patients receiving outpatient and inpatient care at Shenzhen People’s Hospital in China for various physical discomforts.The patients completed multiple questionnaires,including the Neuro-11,Short Form 36 Health Survey,Patient Health Questionnaire 15 items,Hamilton Anxiety Scale and Hamilton Depression Scale.Psychiatry-trained clinicians conducted structured interviews and clinical examinations to establish a gold standard diagnosis of SSD.Results The Neuro-11 demonstrated strong content reliability and structural consistency,correlating significantly with internationally recognised and widely used questionnaires.Despite its brevity,the Neuro-11 exhibited significant correlations with other questionnaires.A test-retest analysis yielded a correlation coefficient of 1.00,Spearman-Brown coefficient of 0.64 and Cronbach’sαcoefficient of 0.72,indicating robust content reliability and internal consistency.Confirmatory factor analysis confirmed the validity of the three-dimensional structure(p<0.001,comparative fit index=0.94,Tucker-Lewis index=0.92,root mean square error of approximation=0.06,standardised root mean square residual=0.04).The threshold of the Neuro-11 is set at 10 points based on the maximum Youden’s index from the receiver operating characteristic curve analysis.In terms of diagnostic efficacy,the Neuro-11 has an area under the curve of 0.67.展开更多
BACKGROUND Restrictive practices(RPs)are defined by measures linked to physical and chemical restraints to reduce the movement or control behaviours during any emergency.Seclusion is an equal part of RPs intended to i...BACKGROUND Restrictive practices(RPs)are defined by measures linked to physical and chemical restraints to reduce the movement or control behaviours during any emergency.Seclusion is an equal part of RPs intended to isolate and reduce the sensory stimulation to safeguard the patient and those within the vicinity.Using interventions by way of virtual reality(VR)could assist with reducing the need for RPs as it could help reduce anxiety or agitation by way of placing users into realistic and immersive environments.This could also aid staff to and change current RPs.AIM To assess the feasibility and effectiveness of using a VR platform to provide reduction in RP training.METHODS A randomised controlled feasibility study,accompanied by evaluations at 1 month and 6 months,was conducted within inpatient psychiatric wards at Southern Health National Health Service Foundation Trust,United Kingdom.Virti VR scenarios were used on VR headsets to provide training on reducing RPs in 3 inpatient psychiatric wards.Outcome measures included general self-efficacy scale,generalised anxiety disorder assessment 7(GAD-7),Burnout Assessment Tool 12,the Everyday Discrimination Scale,and the Compassionate Engagement and Action Scale.RESULTS Findings revealed statistically significant differences between the VR and treatment as usual groups,in the Everyday Discrimination Scale items Q8 and Q9:P=0.023 and P=0.040 respectively,indicating higher levels of perceived discrimination in the VR group.There were no significant differences between groups in terms of general self-efficacy,generalised anxiety disorder assessment 9,and Burnout Assessment Tool 12 scores.A significant difference was observed within the VR group for compassionate engagement from others(P=0.005)over time.Most respondents recorded System Usability Scale scores above 70,with an average score of 71.79.There was a significant reduction in rates of RPs in the VR group vs treatment as usual group with a fluctuating variability observed in the VR group likely due to external factors not captured in the study.CONCLUSION Ongoing advancement of VR technology enables the possibility of creating scenarios and simulations tailored to healthcare environments that empower staff by providing more comprehensive and effective training for handling situations.展开更多
We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models...We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models’predictions.We first examine the stationary of the dataset and use ARIMA(0,1,1)to make predictions about the stock price during the pandemic,then we train the Prophet model using the stock price before January 1,2021,and predict the stock price after January 1,2021,to present.We also make a comparison of the prediction graphs of the two models.The empirical results show that the ARIMA model has a better performance in predicting Google’s stock price during the pandemic.展开更多
The era of big data brings opportunities and challenges to developing new statistical methods and models to evaluate social programs or economic policies or interventions. This paper provides a comprehensive review on...The era of big data brings opportunities and challenges to developing new statistical methods and models to evaluate social programs or economic policies or interventions. This paper provides a comprehensive review on some recent advances in statistical methodologies and models to evaluate programs with high-dimensional data. In particular, four kinds of methods for making valid statistical inferences for treatment effects in high dimensions are addressed. The first one is the so-called doubly robust type estimation, which models the outcome regression and propensity score functions simultaneously. The second one is the covariate balance method to construct the treatment effect estimators. The third one is the sufficient dimension reduction approach for causal inferences. The last one is the machine learning procedure directly or indirectly to make statistical inferences to treatment effect. In such a way, some of these methods and models are closely related to the de-biased Lasso type methods for the regression model with high dimensions in the statistical literature. Finally, some future research topics are also discussed.展开更多
BACKGROUND Recommendations for psychotherapy have evolved over the years,with cognitive behavioral therapy(CBT)taking precedence since its inception within clinical guidelines in the United Kingdom and United States.T...BACKGROUND Recommendations for psychotherapy have evolved over the years,with cognitive behavioral therapy(CBT)taking precedence since its inception within clinical guidelines in the United Kingdom and United States.The use of CBT for severe mental illness is now more common globally.AIM To investigate the feasibility and acceptability of a culturally adapted,CBT-based,third-wave therapy manual using the Comprehend,Cope,and Connect approach with individuals from a diverse population presenting to primary and secondary healthcare services.METHODS A pilot study was used to assess the feasibility and acceptability of the manualised intervention.Outcome measures were evaluated at baseline,post-intervention and 12 wk-follow up.32 participants with mental health conditions aged 20-53 years were recruited.Assessments were completed at three time points,using Clinical Outcomes in Routine Evaluation(CORE),Hospital Anxiety and Depression Scale(HADS),Bradford Somatic Inventory and World Health Organization Disability Assessment Schedule 2.0(WHODAS).The Patient Experience Questionnaire was completed post-treatment.RESULTS Repeated measures of analysis of variance associated with HADS depression,F(2,36)=12.81,P<0.001,partialη^(2)=0.42 and HADS anxiety scores,F(2,26)=9.93,P<0.001,partialη^(2)=0.36;CORE total score and WHODAS both showed significant effect F(1.25,18.72)=14.98,P<0.001,partialη^(2)=0.5.and F(1.29,14.18)=6.73,P<0.001,partialη^(2)=0.38 respectively.CONCLUSION These results indicate the effectiveness and acceptability of the culturally adapted,CBT-based,third-wave therapy manual intervention among minoritized groups with moderate effect sizes.Satisfaction levels and acceptability were highly rated.The viability and cost-effectiveness of this approach should be explored further to support universal implementation across healthcare systems.展开更多
Background Chronic diseases represent a growing public health challenge globally,particularly in developing regions like Sub-Saharan Africa.Somaliland faces a dual burden of communicable and non-communicable diseases ...Background Chronic diseases represent a growing public health challenge globally,particularly in developing regions like Sub-Saharan Africa.Somaliland faces a dual burden of communicable and non-communicable diseases amidst post-conflict recovery,yet data on non-communicable disease(NCD)prevalence and determinants remain scarce.This study aimed to ascertain the prevalence and identify socio-demographic factors associated with self-reported chronic diseases among adults in Somaliland.Methods This cross-sectional study utilized data from the Somaliland Health and Demographic Survey(SLHDS)2020.The final sample of 11,153 adults had a highly skewed age distribution(94.6%aged 35–44),a primary limitation of this analysis.The outcome was a self-reported physician diagnosis of any chronic disease.A multilevel mixed-effect logistic regression model was employed to identify significant determinants while adjusting for confounders.Results The analysis identified several factors associated with reporting a chronic disease.Higher odds were observed among female-headed households(adjusted odds ratio[AOR]:1.26;95%CI:1.06–1.48)and divorced individuals(AOR:1.92;95%CI:1.55–2.37).Conversely,lower odds were associated with higher education(AOR:0.53;95%CI:0.34–0.82)and nomadic residence(AOR:0.34;95%CI:0.22–0.51).Unexpectedly,lack of electricity and no savings were also associated with lower odds,likely reflecting diagnostic access bias and reverse causation.Conclusion Findings suggest that self-reported chronic diseases are associated with specific socio-demographic vulnerabilities in Somaliland.The results should be interpreted with extreme caution and viewed as hypothesis-generating at best.Future research using objective measures and representative sampling is urgently needed to validate these associations and accurately quantify the NCD burden.展开更多
Gaussian process(GP)is a stochastic process that has been successfully applied in finance,black-box modeling of biosystems,machine learning,geostatistics,multitask learning or robotics and reinforcement learning.Effec...Gaussian process(GP)is a stochastic process that has been successfully applied in finance,black-box modeling of biosystems,machine learning,geostatistics,multitask learning or robotics and reinforcement learning.Effectively estimating the spectral density function(SDF)and degree of memory(DOM)of a long-memory stationary GP(LMSGP)is needed to get accurate information about the process.The practice demonstrated that the periodogram estimator(PE)and lag window estimator(LWE)that are the extremely used estimators of the SDF and DOM have some drawbacks,especially for LMSGPs.The behaviors of the PEs and LWEs are soundly investigated numerically;however,the theoretical justifications are limited and thus the challenge to improve them is still daunting.This paper gives a closer look at the theoretical justifications of the efficiency of the LWEs that provides new sufficient conditions(NSCs)for improving the LWEs of the SDF and DOM for LMSGPs.The precision,the convergence rate of the bias and variance,and the asymptotic distributions of the LWEs under the NSCs are studied.A comparison study among the LWEs under the NSCs,the LWEs without the NSCs and the PEs is given to investigate the significance of the NSCs.The main theoretical and simulation results show that:the LWEs under theNSCs are asymptotically unbiased and consistent and better than the LWEswithout the NSCs and the PEs,and the asymptotic distributions of the LWEs under the NSCs are chi-square for SDF and normal for DOM.展开更多
In this paper,we study the nonlinear Choquard equation△^(2)u-△u+(1+λa(x))u=(Rα*|u|^(P))|u|^(p-2)u on a Cayley graph of a discrete group of polynomial growth with the homogeneous dimension N≥1,whereα∈(0,N),p>...In this paper,we study the nonlinear Choquard equation△^(2)u-△u+(1+λa(x))u=(Rα*|u|^(P))|u|^(p-2)u on a Cayley graph of a discrete group of polynomial growth with the homogeneous dimension N≥1,whereα∈(0,N),p>(N+α)/N,λis a postive parameter and Rαstands for the Green's function of the discrete fractional Laplacian,which has no singularity at the origin but has same asymptotics as the Riesz potential at infinity.Under some assumptions onα(x),we establish the existence and asymptotic behavior of ground state solutions for the nonlinear Choquard equation by the method of Nehari manifold.展开更多
For non-stationary complex dynamic systems,a standardized algorithm is developed to compute time correlation functions,addressing the limitations of traditional methods reliant on the stationary assumption.The propose...For non-stationary complex dynamic systems,a standardized algorithm is developed to compute time correlation functions,addressing the limitations of traditional methods reliant on the stationary assumption.The proposed algorithm integrates two-point and multi-point time correlation functions into a unified framework.Further,it is verified by a practical application in complex financial systems,demonstrating its potential in various complex dynamic systems.展开更多
Background: Minimally invasive surgery is becoming increasingly popular in the field of pancreatic surgery. However, there are few studies of robotic distal pancreatectomy(RDP) for pancreatic ductal adenocarcinoma(PDA...Background: Minimally invasive surgery is becoming increasingly popular in the field of pancreatic surgery. However, there are few studies of robotic distal pancreatectomy(RDP) for pancreatic ductal adenocarcinoma(PDAC). This study aimed to investigate the efficacy and feasibility of RDP for PDAC. Methods: Patients who underwent RDP or laparoscopic distal pancreatectomy(LDP) for PDAC between January 2015 and September 2020 were reviewed. Propensity score matching analyses were performed. Results: Of the 335 patients included in the study, 24 underwent RDP and 311 underwent LDP. A total of 21 RDP patients were matched 1:1 with LDP patients. RDP was associated with longer operative time(209.7 vs. 163.2 min;P = 0.003), lower open conversion rate(0% vs. 4.8%;P < 0.001), higher cost(15 722 vs. 12 699 dollars;P = 0.003), and a higher rate of achievement of an R0 resection margin(90.5% vs. 61.9%;P = 0.042). However, postoperative pancreatic fistula grade B or C showed no significant intergroup difference(9.5% vs. 9.5%). The median disease-free survival(34.5 vs. 17.3 months;P = 0.588) and overall survival(37.7 vs. 21.9 months;P = 0.171) were comparable between the groups. Conclusions: RDP is associated with longer operative time, a higher cost of surgery, and a higher likelihood of achieving R0 margins than LDP.展开更多
The stock market is an important economic information center.The economic benefits generated by stock price prediction have attracted much attention.Although the stock market cannot be predicted accurately,the stock m...The stock market is an important economic information center.The economic benefits generated by stock price prediction have attracted much attention.Although the stock market cannot be predicted accurately,the stock market’s prediction of the trend of stock prices helps in grasping the operation law of the stock market and the influence mechanism on the economy.The autoregressive integrated moving average(ARIMA)model is one of the most widely accepted and used time series forecasting models.Therefore,this paper first compares the return on investment(ROI)of Apple and Tesla,revealing that the ROI of Tesla is much greater than that of Apple,and subsequently focuses on ARIMA model’s prediction on the available time series data,thus concluding that the ARIMA model is better than the Naïve method in predicting the change in Tesla’s stock price trend.展开更多
The Berry-Esseen bound provides an upper bound on the Kolmogorov distance between a random variable and the normal distribution.In this paper,we establish Berry-Esseen bounds with optimal rates for self-normalized sum...The Berry-Esseen bound provides an upper bound on the Kolmogorov distance between a random variable and the normal distribution.In this paper,we establish Berry-Esseen bounds with optimal rates for self-normalized sums of locally dependent random variables,assuming only a second-moment condition.Our proof leverages Stein's method and introduces a novel randomized concentration inequality,which may also be of independent interest for other applications.Our main results have applied to self-normalized sums of m-dependent random variables and graph dependency models.展开更多
基金This work was supported by grants from the National Natural Science Foundation of China,with No.NSFC62006109 and NSFC12031005.
文摘Purpose:In recent decades,with the availability of large-scale scientific corpus datasets,difference-in-difference(DID)is increasingly used in the science of science and bibliometrics studies.DID method outputs the unbiased estimation on condition that several hypotheses hold,especially the common trend assumption.In this paper,we gave a systematic demonstration of DID in the science of science,and the potential ways to improve the accuracy of DID method.Design/methodology/approach:At first,we reviewed the statistical assumptions,the model specification,and the application procedures of DID method.Second,to improve the necessary assumptions before conducting DID regression and the accuracy of estimation,we introduced some matching techniques serving as the pre-selecting step for DID design by matching control individuals who are equivalent to those treated ones on observational variables before the intervention.Lastly,we performed a case study to estimate the effects of prizewinning on the scientific performance of Nobel laureates,by comparing the yearly citation impact after the prizewinning year between Nobel laureates and their prizewinning-work coauthors.Findings:We introduced the procedures to conduct a DID estimation and demonstrated the effectiveness to use matching method to improve the results.As a case study,we found that there are no significant increases in citations for Nobel laureates compared to their prizewinning coauthors.Research limitations:This study ignored the rigorous mathematical deduction parts of DID,while focused on the practical parts.Practical implications:This work gives experimental practice and potential guidelines to use DID method in science of science and bibliometrics studies.Originality/value:This study gains insights into the usage of econometric tools in science of science.
文摘In this study,we propose a Gram-Charlier expansion approach to investigate the impact of skewness and kurtosis on production and hedging decisions.Consistent with the existing literature,we find that skewness and kurtosis do not affect decisions regarding optimal production;however,they significantly influence optimal hedging decisions.We observe that positive skewness with platykurtic spot prices or negative skewness with leptokurtic spot prices often leads to over-hedging when the initial forward contract price exceeds its expected value.Conversely,under-hedging is expected when the initial forward contract price falls below its expected value.In other conditions,skewness can either promote or impede speculative future trading.Using the Gram-Charlier expansion of the spot price density function,we find that optimal future positions depend on forward prices,the hedgers’risk preference,and the spot price distribution.Simulations validate our findings on the impact of skewness and kurtosis on future hedging.Finally,we analyze of a cotton storage and forward contracting dataset to illustrate the application of our methodology and support our theoretical results.
文摘The integration of artificial intelligence(AI)into various sectors has undoubtedly brought about numerous benefits,from increased efficiency to innovative problem‐solving.The growing influence of AI across several industries may help to achieve the sustainable development goals(SDGs).However,due to the AI revolution happening in industries across the globe,older employees are often confronted with significant hurdles in keeping pace with these changes.The threat of job displacement looms large as automation driven by AI encroaches upon routine tasks previously performed by human workers.Job insecurity,that is,worry of losing one's job encompasses anxiety,and uneasiness,and affects the mental health of employees.To address these challenges and empower older employees in the era of open AI,it is imperative that organizations implement targeted strategies tailored to their unique needs and circumstances.Employees use the opportunities for continued education provided to them with company support to prevent unwanted effects.organizations can create an inclusive and supportive environment where older employees are empowered to embrace the opportunities presented by AI while leveraging their experience and expertise to drive innovation and success.
基金supported by the National Key R&D Program of China(No.2023YFF1304002-05)the National Social Science Fund of China(No.22BTJ005)the National Natural Science Foundation of China(No.32572049)。
文摘Remote sensing plays a pivotal role in forest inventory by enabling efficient large-scale monitoring while minimizing fieldwork costs.However,missing values pose a critical challenge in remote sensing applications,as ignoring or mishandling such data gaps can introduce systematic bias into the estimation of target variables for natural resource monitoring.This can lead to cascading errors that propagate through forest and ecosystem management decisions,ultimately hindering progress toward sustainable forest management,biodiversity conservation,and climate change mitigation strategies.This study aims to propose and demonstrate a procedure that employs hybrid estimators to address the limitations of missing remotely sensed data in forest inventory,using Landsat 7 ETM+SLC-off data as an archived source for forest resource monitoring as a case in point.We compared forest inventory estimates from the hybrid estimator with those from a conventional model-based(CMB)estimator using Sentinel-2 data without missing values.Monte Carlo simulations revealed three key findings:(1)The hybrid estimator,leveraging missing-data remote sensing represented by Landsat 7 ETM+SLCoff data,achieved a sampling precision of over 90%,meeting China's national standard for the National Forest Inventory(NFI);(2)The hybrid estimator demonstrated comparable efficiency to the CMB estimator;(3)The uncertainty associated with hybrid estimators was primarily dominated by model parameter estimation,which could be effectively mitigated by slightly increasing the training sample size or refining model specification.Overall,in forest inventory,the hybrid estimator can surmount the limitations posed by missing values in remotely sensed auxiliary data,effectively balancing cost-effectiveness and flexibility.
基金supported by the National Social Science Fund of China(Grand No.21XTJ001).
文摘A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment effect and interim result.For hypotheses and reversed hypotheses under normal models,we obtain analytical expressions of the ROC curves of the CCP,find optimal ROC curves of the CCP,investigate the superiority of the ROC curves of the CCP,calculate critical values of the False Positive Rate(FPR),True Positive Rate(TPR),and cutoff of the optimal CCP,and give go/no go decisions at the interim of the optimal CCP.In addition,extensive numerical experiments are carried out to exemplify our theoretical results.Finally,a real data example is performed to illustrate the go/no go decisions of the optimal CCP.
基金supported by grants from Shenzhen Polytechnic University Research(Fund No.6025310042 K)the National Natural Science Foundation of China(No.NSFC62006109 and NSFC12031005).
文摘Purpose:This study investigates the impact of domestic mobility on Chinese scientists’academic performance and explores the predictors influencing their chances of moving to more prestigious institutions.Design/methodology/approach:Using publication and affiliation data from OpenAlex,we identified 2,896 scientists who relocated between cities in China from 2014 to 2017.We applied propensity score matching(PSM)to compare their academic outcomes post-mobility with a matched group of non-mobile peers.Multiple performance metrics were examined,including publication count,citation impact,number of collaborators,and university prestige.Ordered logistic regression was used to analyze factors influencing moves to higher-level institutions.Findings:Mobility enhances collaboration by increasing the number of coauthors but is associated with a short-term decline in citation impact.Scientists were more likely to move to lower-prestige universities.However,prior collaboration breadth and citation count positively predicted transitions to more prestigious institutions,while the number of publications did not.Research limitations:This study focuses on intra-national mobility within China from 2014 to 2017 and relies on quantitative data,lacking personal or qualitative variables such as gender,discipline-specific norms,or institutional culture.Data coverage for Chinese-language publications may also be limited.Practical implications:This research provides insights into academic hiring patterns and the trade-offs involved in scientist mobility.It offers valuable guidance for institutions aiming to enhance faculty recruitment and retention,as well as for researchers considering career transitions.Originality/value:This is a quantitative analysis of domestic scientist mobility in China using matched comparison and multi-dimensional academic indicators.The integration of university prestige metrics(Double First-Class and citation-based rankings)offers a nuanced view of career dynamics within the Chinese higher education system.
基金supported by the National Social Science Foundation of China(grant No.CIA200270)National Natural Science Foundation of China(grant No.62006109 and 12031005).
文摘Purpose:This study investigates factors associated with scientific recognition,examining how collaboration networks influence the path to ACM fellowship.Design/methodology/approach:We analyzed 1,497 ACM fellows(1994-2023)using linear regression on 286,791 publication records,examining co-authorship patterns and institutional overlaps while controlling for productivity metrics.Findings:Collaboration with ACM fellows among new electees increased from 43%to over 90%.Collaborating with ACM fellows is associated with achieving fellowship 3.8 years earlier,with frequent,recent collaborations and prestigious collaborators exhibiting even shorter time intervals to recognition.Gender and institutional factors also significantly impact timing.Research limitations:The study is correlational,focuses on one society,and may not capture all forms of scientific contribution beyond traditional metrics.Practical implications:Current processes may favor well-connected candidates.Reforms should increase transparency and expand recognition criteria to address biases and promote inclusivity.Originality/value:This provides the first comprehensive three-decade analysis of ACM fellowship patterns,revealing the growing importance of strategic networking in scientific recognition and offering evidencebased recommendations for more inclusive evaluation processes.
文摘BACKGROUND Pancreatic ductal adenocarcinoma(PDAC)is a serious disease with a poor prognosis.Only a minority of patients undergo surgery due to the advanced stage of the disease,and patients with early-stage disease,who are expected to have a better prognosis,often experience recurrence.Thus,it is important to identify the risk factors for early recurrence and to develop an adequate treatment plan.AIM To evaluate the predictive factors associated with the early recurrence of earlystage PDAC.METHODS This study enrolled 407 patients with stage I PDAC undergoing upfront surgical resection between January 2000 and April 2016.Early recurrence was defined as a diagnosis of recurrence within 6 mo of surgery.The optimal cutoff values were determined by receiver operating characteristic(ROC)analyses.Univariate and multivariate analyses were performed to identify the risk factors for early recurrence.RESULTS Of the 407 patients,98 patients(24.1%)experienced early disease recurrence:26(26.5%)local and 72(73.5%)distant sites.In total,253(62.2%)patients received adjuvant chemotherapy.On ROC curve analysis,the optimal cutoff values for early recurrence were 70 U/mL and 2.85 cm for carbohydrate antigen 19-9(CA 19-9)levels and tumor size,respectively.Of the 181 patients with CA 19-9 level>70 U/mL,59(32.6%)had early recurrence,compared to 39(17.4%)of 226 patients with CA 19-9 level≤70 U/mL(P<0.001).Multivariate analysis revealed that CA 19-9 level>70 U/mL(P=0.006),tumor size>2.85 cm(P=0.004),poor differentiation(P=0.008),and non-adjuvant chemotherapy(P=0.025)were significant risk factors for early recurrence in early-stage PDAC.CONCLUSION Elevated CA 19-9 level(cutoff value>70 U/mL)can be a reliable predictive factor for early recurrence in early-stage PDAC.As adjuvant chemotherapy can prevent early recurrence,it should be recommended for patients susceptible to early recurrence.
基金This research was supported by the following funds:Shenzhen Science and Technology Innovation Commission(KCXFZ20201221173400001,KCXFZ20201221173411032,SGDX20210823103805042)Natural Science Fund of Guangdong Province(2021A1515010983)Shenzhen Key Medical Discipline Construction Fund(no.SZXK005).
文摘Background Somatic symptom disorder(SSD)commonly presents in general hospital settings,posing challenges for healthcare professionals lacking specialised psychiatric training.The Neuro-11 Neurosis Scale(Neuro-11)offers promise in screening and evaluating psychosomatic symptoms,comprising 11 concise items across three dimensions:somatic symptoms,negative emotions and adverse events.Prior research has validated the scale’s reliability,validity and theoretical framework in somatoform disorders,indicating its potential as a valuable tool for SSD screening in general hospitals.Aims This study aimed to establish the reliability,validity and threshold of the Neuro-11 by comparing it with standard questionnaires commonly used in general hospitals for assessing SSD.Through this comparative analysis,we aimed to validate the effectiveness and precision of the Neuro-11,enhancing its utility in clinical settings.Methods Between November 2020 and December 2021,data were collected from 731 patients receiving outpatient and inpatient care at Shenzhen People’s Hospital in China for various physical discomforts.The patients completed multiple questionnaires,including the Neuro-11,Short Form 36 Health Survey,Patient Health Questionnaire 15 items,Hamilton Anxiety Scale and Hamilton Depression Scale.Psychiatry-trained clinicians conducted structured interviews and clinical examinations to establish a gold standard diagnosis of SSD.Results The Neuro-11 demonstrated strong content reliability and structural consistency,correlating significantly with internationally recognised and widely used questionnaires.Despite its brevity,the Neuro-11 exhibited significant correlations with other questionnaires.A test-retest analysis yielded a correlation coefficient of 1.00,Spearman-Brown coefficient of 0.64 and Cronbach’sαcoefficient of 0.72,indicating robust content reliability and internal consistency.Confirmatory factor analysis confirmed the validity of the three-dimensional structure(p<0.001,comparative fit index=0.94,Tucker-Lewis index=0.92,root mean square error of approximation=0.06,standardised root mean square residual=0.04).The threshold of the Neuro-11 is set at 10 points based on the maximum Youden’s index from the receiver operating characteristic curve analysis.In terms of diagnostic efficacy,the Neuro-11 has an area under the curve of 0.67.
文摘BACKGROUND Restrictive practices(RPs)are defined by measures linked to physical and chemical restraints to reduce the movement or control behaviours during any emergency.Seclusion is an equal part of RPs intended to isolate and reduce the sensory stimulation to safeguard the patient and those within the vicinity.Using interventions by way of virtual reality(VR)could assist with reducing the need for RPs as it could help reduce anxiety or agitation by way of placing users into realistic and immersive environments.This could also aid staff to and change current RPs.AIM To assess the feasibility and effectiveness of using a VR platform to provide reduction in RP training.METHODS A randomised controlled feasibility study,accompanied by evaluations at 1 month and 6 months,was conducted within inpatient psychiatric wards at Southern Health National Health Service Foundation Trust,United Kingdom.Virti VR scenarios were used on VR headsets to provide training on reducing RPs in 3 inpatient psychiatric wards.Outcome measures included general self-efficacy scale,generalised anxiety disorder assessment 7(GAD-7),Burnout Assessment Tool 12,the Everyday Discrimination Scale,and the Compassionate Engagement and Action Scale.RESULTS Findings revealed statistically significant differences between the VR and treatment as usual groups,in the Everyday Discrimination Scale items Q8 and Q9:P=0.023 and P=0.040 respectively,indicating higher levels of perceived discrimination in the VR group.There were no significant differences between groups in terms of general self-efficacy,generalised anxiety disorder assessment 9,and Burnout Assessment Tool 12 scores.A significant difference was observed within the VR group for compassionate engagement from others(P=0.005)over time.Most respondents recorded System Usability Scale scores above 70,with an average score of 71.79.There was a significant reduction in rates of RPs in the VR group vs treatment as usual group with a fluctuating variability observed in the VR group likely due to external factors not captured in the study.CONCLUSION Ongoing advancement of VR technology enables the possibility of creating scenarios and simulations tailored to healthcare environments that empower staff by providing more comprehensive and effective training for handling situations.
文摘We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models’predictions.We first examine the stationary of the dataset and use ARIMA(0,1,1)to make predictions about the stock price during the pandemic,then we train the Prophet model using the stock price before January 1,2021,and predict the stock price after January 1,2021,to present.We also make a comparison of the prediction graphs of the two models.The empirical results show that the ARIMA model has a better performance in predicting Google’s stock price during the pandemic.
基金Supported by the National Natural Science Foundation of China(71631004, 72033008)National Science Foundation for Distinguished Young Scholars(71625001)Science Foundation of Ministry of Education of China(19YJA910003)。
文摘The era of big data brings opportunities and challenges to developing new statistical methods and models to evaluate social programs or economic policies or interventions. This paper provides a comprehensive review on some recent advances in statistical methodologies and models to evaluate programs with high-dimensional data. In particular, four kinds of methods for making valid statistical inferences for treatment effects in high dimensions are addressed. The first one is the so-called doubly robust type estimation, which models the outcome regression and propensity score functions simultaneously. The second one is the covariate balance method to construct the treatment effect estimators. The third one is the sufficient dimension reduction approach for causal inferences. The last one is the machine learning procedure directly or indirectly to make statistical inferences to treatment effect. In such a way, some of these methods and models are closely related to the de-biased Lasso type methods for the regression model with high dimensions in the statistical literature. Finally, some future research topics are also discussed.
文摘BACKGROUND Recommendations for psychotherapy have evolved over the years,with cognitive behavioral therapy(CBT)taking precedence since its inception within clinical guidelines in the United Kingdom and United States.The use of CBT for severe mental illness is now more common globally.AIM To investigate the feasibility and acceptability of a culturally adapted,CBT-based,third-wave therapy manual using the Comprehend,Cope,and Connect approach with individuals from a diverse population presenting to primary and secondary healthcare services.METHODS A pilot study was used to assess the feasibility and acceptability of the manualised intervention.Outcome measures were evaluated at baseline,post-intervention and 12 wk-follow up.32 participants with mental health conditions aged 20-53 years were recruited.Assessments were completed at three time points,using Clinical Outcomes in Routine Evaluation(CORE),Hospital Anxiety and Depression Scale(HADS),Bradford Somatic Inventory and World Health Organization Disability Assessment Schedule 2.0(WHODAS).The Patient Experience Questionnaire was completed post-treatment.RESULTS Repeated measures of analysis of variance associated with HADS depression,F(2,36)=12.81,P<0.001,partialη^(2)=0.42 and HADS anxiety scores,F(2,26)=9.93,P<0.001,partialη^(2)=0.36;CORE total score and WHODAS both showed significant effect F(1.25,18.72)=14.98,P<0.001,partialη^(2)=0.5.and F(1.29,14.18)=6.73,P<0.001,partialη^(2)=0.38 respectively.CONCLUSION These results indicate the effectiveness and acceptability of the culturally adapted,CBT-based,third-wave therapy manual intervention among minoritized groups with moderate effect sizes.Satisfaction levels and acceptability were highly rated.The viability and cost-effectiveness of this approach should be explored further to support universal implementation across healthcare systems.
文摘Background Chronic diseases represent a growing public health challenge globally,particularly in developing regions like Sub-Saharan Africa.Somaliland faces a dual burden of communicable and non-communicable diseases amidst post-conflict recovery,yet data on non-communicable disease(NCD)prevalence and determinants remain scarce.This study aimed to ascertain the prevalence and identify socio-demographic factors associated with self-reported chronic diseases among adults in Somaliland.Methods This cross-sectional study utilized data from the Somaliland Health and Demographic Survey(SLHDS)2020.The final sample of 11,153 adults had a highly skewed age distribution(94.6%aged 35–44),a primary limitation of this analysis.The outcome was a self-reported physician diagnosis of any chronic disease.A multilevel mixed-effect logistic regression model was employed to identify significant determinants while adjusting for confounders.Results The analysis identified several factors associated with reporting a chronic disease.Higher odds were observed among female-headed households(adjusted odds ratio[AOR]:1.26;95%CI:1.06–1.48)and divorced individuals(AOR:1.92;95%CI:1.55–2.37).Conversely,lower odds were associated with higher education(AOR:0.53;95%CI:0.34–0.82)and nomadic residence(AOR:0.34;95%CI:0.22–0.51).Unexpectedly,lack of electricity and no savings were also associated with lower odds,likely reflecting diagnostic access bias and reverse causation.Conclusion Findings suggest that self-reported chronic diseases are associated with specific socio-demographic vulnerabilities in Somaliland.The results should be interpreted with extreme caution and viewed as hypothesis-generating at best.Future research using objective measures and representative sampling is urgently needed to validate these associations and accurately quantify the NCD burden.
基金supported by the UIC Research Grants with No.of(R201810,R201912 and R202010)the Curriculum Development and Teaching Enhancement with No.of(UICR0400046-21CTL)+1 种基金the Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science,BNU-HKBU United International College with No.of(2022B1212010006)in part by Guangdong Higher Education Upgrading Plan(2021-2025)with No.of(UIC R0400001-22).
文摘Gaussian process(GP)is a stochastic process that has been successfully applied in finance,black-box modeling of biosystems,machine learning,geostatistics,multitask learning or robotics and reinforcement learning.Effectively estimating the spectral density function(SDF)and degree of memory(DOM)of a long-memory stationary GP(LMSGP)is needed to get accurate information about the process.The practice demonstrated that the periodogram estimator(PE)and lag window estimator(LWE)that are the extremely used estimators of the SDF and DOM have some drawbacks,especially for LMSGPs.The behaviors of the PEs and LWEs are soundly investigated numerically;however,the theoretical justifications are limited and thus the challenge to improve them is still daunting.This paper gives a closer look at the theoretical justifications of the efficiency of the LWEs that provides new sufficient conditions(NSCs)for improving the LWEs of the SDF and DOM for LMSGPs.The precision,the convergence rate of the bias and variance,and the asymptotic distributions of the LWEs under the NSCs are studied.A comparison study among the LWEs under the NSCs,the LWEs without the NSCs and the PEs is given to investigate the significance of the NSCs.The main theoretical and simulation results show that:the LWEs under theNSCs are asymptotically unbiased and consistent and better than the LWEswithout the NSCs and the PEs,and the asymptotic distributions of the LWEs under the NSCs are chi-square for SDF and normal for DOM.
文摘In this paper,we study the nonlinear Choquard equation△^(2)u-△u+(1+λa(x))u=(Rα*|u|^(P))|u|^(p-2)u on a Cayley graph of a discrete group of polynomial growth with the homogeneous dimension N≥1,whereα∈(0,N),p>(N+α)/N,λis a postive parameter and Rαstands for the Green's function of the discrete fractional Laplacian,which has no singularity at the origin but has same asymptotics as the Riesz potential at infinity.Under some assumptions onα(x),we establish the existence and asymptotic behavior of ground state solutions for the nonlinear Choquard equation by the method of Nehari manifold.
基金Project supported by the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(Grant No.GZC20231050)the National Natural Science Foundation of China(Grant Nos.12175193 and 11905183)the 13th Five-year plan for Education Science Funding of Guangdong Province(Grant No.2021GXJK349)。
文摘For non-stationary complex dynamic systems,a standardized algorithm is developed to compute time correlation functions,addressing the limitations of traditional methods reliant on the stationary assumption.The proposed algorithm integrates two-point and multi-point time correlation functions into a unified framework.Further,it is verified by a practical application in complex financial systems,demonstrating its potential in various complex dynamic systems.
文摘Background: Minimally invasive surgery is becoming increasingly popular in the field of pancreatic surgery. However, there are few studies of robotic distal pancreatectomy(RDP) for pancreatic ductal adenocarcinoma(PDAC). This study aimed to investigate the efficacy and feasibility of RDP for PDAC. Methods: Patients who underwent RDP or laparoscopic distal pancreatectomy(LDP) for PDAC between January 2015 and September 2020 were reviewed. Propensity score matching analyses were performed. Results: Of the 335 patients included in the study, 24 underwent RDP and 311 underwent LDP. A total of 21 RDP patients were matched 1:1 with LDP patients. RDP was associated with longer operative time(209.7 vs. 163.2 min;P = 0.003), lower open conversion rate(0% vs. 4.8%;P < 0.001), higher cost(15 722 vs. 12 699 dollars;P = 0.003), and a higher rate of achievement of an R0 resection margin(90.5% vs. 61.9%;P = 0.042). However, postoperative pancreatic fistula grade B or C showed no significant intergroup difference(9.5% vs. 9.5%). The median disease-free survival(34.5 vs. 17.3 months;P = 0.588) and overall survival(37.7 vs. 21.9 months;P = 0.171) were comparable between the groups. Conclusions: RDP is associated with longer operative time, a higher cost of surgery, and a higher likelihood of achieving R0 margins than LDP.
文摘The stock market is an important economic information center.The economic benefits generated by stock price prediction have attracted much attention.Although the stock market cannot be predicted accurately,the stock market’s prediction of the trend of stock prices helps in grasping the operation law of the stock market and the influence mechanism on the economy.The autoregressive integrated moving average(ARIMA)model is one of the most widely accepted and used time series forecasting models.Therefore,this paper first compares the return on investment(ROI)of Apple and Tesla,revealing that the ROI of Tesla is much greater than that of Apple,and subsequently focuses on ARIMA model’s prediction on the available time series data,thus concluding that the ARIMA model is better than the Naïve method in predicting the change in Tesla’s stock price trend.
基金supported by the Singapore Ministry of Education Academic Research Fund Tier 2(Grant No.MOE2018-T2-2-076)。
文摘The Berry-Esseen bound provides an upper bound on the Kolmogorov distance between a random variable and the normal distribution.In this paper,we establish Berry-Esseen bounds with optimal rates for self-normalized sums of locally dependent random variables,assuming only a second-moment condition.Our proof leverages Stein's method and introduces a novel randomized concentration inequality,which may also be of independent interest for other applications.Our main results have applied to self-normalized sums of m-dependent random variables and graph dependency models.
