This paper aims to explore the application of Extreme Value Theory (EVT) in estimating the conditional extreme quantile for time-to-event outcomes by examining the functional relationship between ambulatory blood pres...This paper aims to explore the application of Extreme Value Theory (EVT) in estimating the conditional extreme quantile for time-to-event outcomes by examining the functional relationship between ambulatory blood pressure trajectories and clinical outcomes in stroke patients. The study utilizes EVT to analyze the functional connection between ambulatory blood pressure trajectories and clinical outcomes in a sample of 297 stroke patients. The 24-hour ambulatory blood pressure measurement curves for every 15 minutes are considered, acknowledging a censored rate of 40%. The findings reveal that the sample mean excess function exhibits a positive gradient above a specific threshold, confirming the heavy-tailed distribution of data in stroke patients with a positive extreme value index. Consequently, the estimated conditional extreme quantile indicates that stroke patients with higher blood pressure measurements face an elevated risk of recurrent stroke occurrence at an early stage. This research contributes to the understanding of the relationship between ambulatory blood pressure and recurrent stroke, providing valuable insights for clinical considerations and potential interventions in stroke management.展开更多
This paper presents a new class of test procedures for two-sample location problem based on subsample quantiles. The class includes Mann-Whitney test as a special case. The asymptotic normality of the class of tests p...This paper presents a new class of test procedures for two-sample location problem based on subsample quantiles. The class includes Mann-Whitney test as a special case. The asymptotic normality of the class of tests proposed is established. The asymptotic relative performance of the proposed class of test with respect to the optimal member of Xie and Priebe (2000) is studied in terms of Pitman efficiency for various underlying distributions.展开更多
Stochastic frontier analysis and quantile regression are the two econometric approaches that have been commonly adopted in the determination of the self-thinning boundary line or surface in two and higher dimensions s...Stochastic frontier analysis and quantile regression are the two econometric approaches that have been commonly adopted in the determination of the self-thinning boundary line or surface in two and higher dimensions since their introduction to the field some 20 years ago.However,the rational for using one method over the other has,in most cases,not been clearly explained perhaps due to a lack of adequate appreciation of differences between the two approaches for delineating the self-thinning surface.Without an adequate understanding of such differences,the most informative analysis may become a missed opportunity,leading to an inefficient use of data,weak statistical inferences and a failure to gain greater insight into the dynamics of plant populations and forest stands that would otherwise be obtained.Using data from 170 plot measurements in even-aged Larix olgensis(A.Henry) plantations across a wide range of site qualities and with different abundances of woody weeds,i.e.naturally regenerated non-crop species,in northeast China,this study compared the two methods in determining the self-thinning surface across eight sample sizes from 30 to 170 with an even interval of 20 observations and also over a range of quantiles through repeated random sampling and estimation.Across all sample sizes and over the quantile range of 0.90 ≤τ≤0.99,the normal-half normal stochastic frontier estimation proved to be superior to quantile regression in statistical efficiency.Its parameter estimates had lower degrees of variability and correspondingly narrower confidence intervals.This greater efficiency would naturally be conducive to making statistical inferences.The estimated self-thinning surface using all 170 observations enveloped about 96.5% of the data points,a degree of envelopment equivalent to a regression quantile estimation with aτ of 0.965.The stochastic frontier estimation was also more objective because it did not involve the subjective selection of a particular value of τ for the favoured self-thinning surface from several mutually intersecting surfaces as in quantile regression.However,quantile regression could still provide a valuable complement to stochastic frontier analysis in the estimation of the self-thinning surface as it allows the examination of the impact of variables other than stand density on different quantiles of stand biomass.展开更多
In this paper, we obtain the joint empirical likelihood confidence regions for a finite number of quantiles under strong mixing samples. As an application of this result, the empirical likelihood confidence intervals ...In this paper, we obtain the joint empirical likelihood confidence regions for a finite number of quantiles under strong mixing samples. As an application of this result, the empirical likelihood confidence intervals for the difference of any two quantiles are also obtained.展开更多
The large sample estimation of standard deviation of logistic distribution employs the asymptotically best linear unbiased estimators based on sample quantiles. The sample quantiles are established from a pair of sing...The large sample estimation of standard deviation of logistic distribution employs the asymptotically best linear unbiased estimators based on sample quantiles. The sample quantiles are established from a pair of single spacing. Finally, a table of the variances and efficiencies of the estimator for 5≤n≤65 is provided and comparison is made with other linear estimators.展开更多
The minimum risk equivariant estimator of a quantile of the common marginal distribution in a multivariate Lomax distribution with unknown location and scale parameters under Linex loss function is considered.
In this paper, the problem of nonparametric estimation of finite population quantile function using multiplicative bias correction technique is considered. A robust estimator of the finite population quantile function...In this paper, the problem of nonparametric estimation of finite population quantile function using multiplicative bias correction technique is considered. A robust estimator of the finite population quantile function based on multiplicative bias correction is derived with the aid of a super population model. Most studies have concentrated on kernel smoothers in the estimation of regression functions. This technique has also been applied to various methods of non-parametric estimation of the finite population quantile already under review. A major problem with the use of nonparametric kernel-based regression over a finite interval, such as the estimation of finite population quantities, is bias at boundary points. By correcting the boundary problems associated with previous model-based estimators, the multiplicative bias corrected estimator produced better results in estimating the finite population quantile function. Furthermore, the asymptotic behavior of the proposed estimators </span><span style="font-family:Verdana;">is</span><span style="font-family:Verdana;"> presented</span><span style="font-family:Verdana;">. </span><span style="font-family:Verdana;">It is observed that the estimator is asymptotically unbiased and statistically consistent when certain conditions are satisfied. The simulation results show that the suggested estimator is quite well in terms of relative bias, mean squared error, and relative root mean error. As a result, the multiplicative bias corrected estimator is strongly suggested for survey sampling estimation of the finite population quantile function.展开更多
The ordinary quantiles for univariate data were successfully generalized to linear modelsin Koenker and Bassett. Regression quantiles provide more specific and more global in-formation on the relationship of two varia...The ordinary quantiles for univariate data were successfully generalized to linear modelsin Koenker and Bassett. Regression quantiles provide more specific and more global in-formation on the relationship of two variables through their distributions. Mosteller andTukey argued that the use of regression quantiles helps to provide a more complete pic-展开更多
On the one hand,we investigate the Bahadur representation for sample quantiles underφ-mixing sequence withφ(n)=O(n^-3)and obtain a rate as O(n-3/4 log n),a.s.On the other hand,by relaxing the condition of mixing coe...On the one hand,we investigate the Bahadur representation for sample quantiles underφ-mixing sequence withφ(n)=O(n^-3)and obtain a rate as O(n-3/4 log n),a.s.On the other hand,by relaxing the condition of mixing coefficients to∑∞n=1φ^1/2(n)<∞,a rate O(n^-1/2(log n)^1/2),a.s.,is also obtained.展开更多
In this article, we put forward a new approach to estimate multiple conditional regression quantiles simultaneously. Unlike the double summation method in most of the literatures, our proposed model allows continuous ...In this article, we put forward a new approach to estimate multiple conditional regression quantiles simultaneously. Unlike the double summation method in most of the literatures, our proposed model allows continuous variety for the quantile level over(0,1). As a result, all the quantile curves can be obtained via a 2-dimensional surface simultaneously. Most importantly, the proposed minimizing criterion can be readily transformed to a linear programming problem. We use tensor product bi-linear quantile smoothing B-splines tofit it. The asymptotic property of the estimator is derived and a real data set is analyzed to demonstrate the proposed method.展开更多
In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This...In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This article considers subgroup analysis methods for longitudinal data containing multiple covariates and biomarkers.We divide subgroups based on whether a linear combination of these biomarkers exceeds a predetermined threshold,and assess the heterogeneity of treatment effects across subgroups using the interaction between subgroups and exposure variables.Quantile regression is used to better characterize the global distribution of the response variable and sparsity penalties are imposed to achieve variable selection of covariates and biomarkers.The effectiveness of our proposed methodology for both variable selection and parameter estimation is verified through random simulations.Finally,we demonstrate the application of this method by analyzing data from the PA.3 trial,further illustrating the practicality of the method proposed in this paper.展开更多
Since its inception,the epsilon distribution has piqued the interest of statisticians.It has been successfully used to solve a variety of statistical problems.In this article,we propose to use the quadratic rank trans...Since its inception,the epsilon distribution has piqued the interest of statisticians.It has been successfully used to solve a variety of statistical problems.In this article,we propose to use the quadratic rank transmutation map mechanism to extend this distribution.This mechanism is not new;it was already used to improve the modeling capabilities of a number of existing distributions.For the original epsilon distribution,we expect the same benefits.As a result,we implement the transmuted epsilon distribution as a flexible three-parameter distribution with a bounded domain.We demonstrate its key features,focusing on the properties of its distributional mechanism and conducting quantile and moment analyses.Applications of the model are presented using two data sets.We also perform a regression analysis based on this distribution.展开更多
As the world’s fourth most populous country,Indonesia presents challenges and opportunities for sustainable energy progress,offering a critical context to investigate green human development(GHD).This study uniquely ...As the world’s fourth most populous country,Indonesia presents challenges and opportunities for sustainable energy progress,offering a critical context to investigate green human development(GHD).This study uniquely contributes to the literature by employing the planetary pressures-adjusted human development index(PHDI)as an indicator of GHD,which integrates environmental impacts into human development.Using static and dynamic econometric methods,including the quantile regression and autoregressive distributed lag model,it explores the impacts of renewable and nonrenewable energy consumption on GHD.The findings demonstrate that renewable energy currently has a detrimental impact on GHD due to its limited adoption and high costs.Conversely,nonrenewable energy positively influences GHD,as it is the primary energy source in the country and is becoming more efficient at reducing emissions.However,the study finds that greater use of renewable energy reduces its adverse effects,suggesting that as renewable energy technologies become more cost-effective and widely implemented,their initial adverse effects could be mitigated,leading to improved long-term GHD outcomes.These findings carry important implications for Indonesia,where the govern‐ment is striving to expand renewable energy capacity while promoting equitable development across its archi‐pelagic regions.They underscore the critical role of energy policy in balancing economic,social,and environmental goals,contributing meaningfully to the country’s sustainable development agenda.展开更多
Branch size is a crucial characteristic,closely linked to both tree growth and wood quality.A review of existing branch size models reveals various approaches,but the ability to estimate branch diameter and length wit...Branch size is a crucial characteristic,closely linked to both tree growth and wood quality.A review of existing branch size models reveals various approaches,but the ability to estimate branch diameter and length within the same whorl remains underexplored.In this study,a total of 77 trees were sampled from Northeast China to model the vertical distribution of branch diameter and length within each whorl along the crown.Several commonly used functions were taken as the alternative model forms,and the quantile regression method was employed and compared with the classical two-step modeling approach.The analysis incorporated stand,tree,and competition factors,with a particular focus on how these factors influence branches of varying sizes.The modified Weibull function was chosen as the optimal model,due to its excellent performance across all quantiles.Eight quantile regression curves(ranging from 0.20 to 0.85)were combined to predict branch diameter,while seven curves(ranging from 0.20 to 0.80)were used for branch length.The results showed that the quantile regression method outperformed the classical approach at model fitting and validation,likely due to its ability to estimate different rates of change across the entire branch size distribution.Lager branches in each whorl were more sensitive to changes in DBH,crown length(CL),crown ratio(CR)and dominant tree height(H_(dom)),while slenderness(HDR)more effectively influenced small and medium-sized branches.The effect of stand basal area(BAS)was relatively consistent across different branch sizes.The findings indicate that quantile regression is a good way not only a more accurate method for predicting branch size but also a valuable tool for understanding how branch growth responds to stand and tree factors.The models developed in this study are prepared to be further integrated into tree growth and yield simulation system,contributing to the assessment and promotion of wood quality.展开更多
Environmental inequality is a prevalent issue in developing countries undergoing urban expansion.Urban expansion induces the formation and evolution of environmental inequality by creating environmental and structural...Environmental inequality is a prevalent issue in developing countries undergoing urban expansion.Urban expansion induces the formation and evolution of environmental inequality by creating environmental and structural conditions that lead to the spatial relocation of environmental hazards and the socio-spatial segregation of different groups in developing countries.This study investigated the spatial patterns and temporal trends of environmental inequality under urban expansion in Guangzhou,a megacity in China.It considered how environmental disparities and socio-demographic attributes interact in terms of industrial pollution exposure using additive semiparametric quantile regression,combined with spatial visualisation,on the basis of the economic and population census data from 1990 to 2020.This study revealed that urban expansion sparked the spatial displacement of environmental risks and the social-spatial differentiation,exposing the peripheral regions and disadvantaged groups to higher environmental risks.A reciprocal transformation occurred between central and peripheral regions,as well as a process of redistributing environmental risks across social space.In the context of urban expansion in developing countries,the causes of environmental inequality shifted from individual socio-economic differences to structural factors,such as industrial layout and social division of labour in cities,leading to the spatial displacement and concealment of environmental inequality.This study provides insights and guidance for policymakers to address the issue of environmental inequality in the context of urban expansion.展开更多
Agriculture has become the backbone of most developing countries in the world, especially Tubah Sub-Division North West region, Cameroon. Following the COVID-19 pandemic and socio-political crisis that hit Cameroon’s...Agriculture has become the backbone of most developing countries in the world, especially Tubah Sub-Division North West region, Cameroon. Following the COVID-19 pandemic and socio-political crisis that hit Cameroon’s economy, there has been a steady increase in food insecurity, which has paved the way for farmers to adopt some sustainable strategies to boost agricultural productivity. Therefore, in trying to find models for survival and the pursuit of growth, farmers adopted some traditional farming methods and the use of local input as a means of sustainability. This study specifically seeks to analyze the effect of sustainability strategies on agricultural productivity in Tubah sub-division North West Region, Cameroon. The data was elicited via a survey questionnaire administered to 202 participating farmers selected from the different farmer organizations in the Tubah sub-division. Using cluster-sampling approach, proximity villages were grouped into four clusters of villages, and stratified sampling was used to select farmers to participate in the study. The objective of the study was achieved using OLS and quantile regression estimation techniques. The result showed evidence that the sustainability strategies implemented by the farmers decreased agricultural productivity in the 25th quantile, and at the 50th and 75th quantile, agricultural productivity still declined. This decline is because of unsustainable agricultural strategies like the use of slash and burn, the use of chemical fertilizers, inadequate capital, low level of education, inadequate farming experience, inadequate income, inadequate farm size, and the type of technology used for farming. Based on the findings, this study recommends that the government should organize training programs and seminars, subsidize farm inputs, grant agricultural loans to farmers, and initiate and support mechanized agriculture to boost agricultural productivity.展开更多
Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread ap...Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread application, often encounter difficulties in handling the complexities of oil production data, which is characterized by non-linear patterns, skewed distributions, and the presence of outliers. To overcome these limitations, deep learning methods have emerged as more robust alternatives. However, while deep neural networks offer improved accuracy, they demand substantial amounts of data for effective training. Conversely, shallow networks with fewer layers lack the capacity to model complex data distributions adequately. To address these challenges, this study introduces a novel hybrid model called Transfer LSTM to GRU (TLTG), which combines the strengths of deep and shallow networks using transfer learning. The TLTG model integrates Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU) to enhance predictive accuracy while maintaining computational efficiency. Gaussian transformation is applied to the input data to reduce outliers and skewness, creating a more normal-like distribution. The proposed approach is validated on datasets from various wells in the Tahe oil field, China. Experimental results highlight the superior performance of the TLTG model, achieving 100% accuracy and faster prediction times (200 s) compared to eight other approaches, demonstrating its effectiveness and efficiency.展开更多
Understanding the urban-rural development mechanism is critical for implementing rural revival and new-type urbanization.However,it remains a challenge to quantify the urban-rural integrated development level(URIDL)an...Understanding the urban-rural development mechanism is critical for implementing rural revival and new-type urbanization.However,it remains a challenge to quantify the urban-rural integrated development level(URIDL)and its impact factors.Hence,we constructed an assessment system for the URIDL from spatial,economic,social,life,and ecological integration.The spatial autocorrelation and Spearman rank correlation coefficients were used to assess the spatiotemporal variation of the URIDL and the trade-off synergistic relationship among the subsystems at the provincial scale in China using socio-economic statistical data from 2000 to 2020.A spatial panel quantile regression model was used to analyze the driving mechanism.The results showed that the URIDL of China increased by 0.19%from 2000 to 2020,and a high-high(H-H)spatial agglomeration pattern occurred in the Yangtze River Delta and the Beijing-Tianjin-Hebei regions.Spatial integration significantly contributed to the other subsystems,whereas economic integration had a significant negative impact on the other subsystems in the eastern coastal and southwestern regions.Per capita Gross Domestic Product(GDP)improved the URIDL,whereas other factors,such as fiscal revenue decentralization,had inhibiting effects.Notably,the impact of factors on URIDL varies across different quantiles.Finally,we proposed policy recommendations for differentiated improvement of URIDL based on its evolution and regional development level during the research period.展开更多
Previous studies have suggested that abnormal hepatobiliary system function may contribute to poor prognosis in patientswith acute coronary syndrome(ACS)and that abnormal hepatobiliary system function may be associate...Previous studies have suggested that abnormal hepatobiliary system function may contribute to poor prognosis in patientswith acute coronary syndrome(ACS)and that abnormal hepatobiliary system function may be associated with per-and polyfluoroalkyl substances(PFAS)exposure.However,there is limited evidence for this association in cardiovascular subpopulations,particularly in the ACS patients.Therefore,we performed this study to evaluate the association between plasma PFAS exposure and hepatobiliary system function biomarkers in patients with ACS.This study included 546 newly diagnosed ACS patients at the Second Hospital of Hebei Medical University,and data on 15 hepatobiliary system function biomarkers were obtained from medical records.Associations between single PFAS and hepatobiliary system function biomarkers were assessed using multiple linear regression models and restricted cubic spline model(RCS),and mixture effects were assessed using the Quantile g-computation model.The results showed that total bile acids(TBA)was negative associated with perfluorohexane sulfonic acid(PFHxS)(-7.69%,95%CI:-12.15%,-3.01%).According to the RCS model,linear associations were found between TBA and PFHxS(P for overall=0.003,P for non-linear=0.234).We also have observed the association between between PFAS congeners and liver enzyme such as aspartate aminotransferase(AST)and α-l-Fucosidase(AFU),but it was not statistically significant after correction.In addition,Our results also revealed an association between prealbumin(PA)and PFAS congeners as well as mixtures.Our findings have provided a piece of epidemiological evidence on associations between PFAS congeners or mixture,and serum hepatobiliary system function biomarkers in ACS patients,which could be a basis for subsequent mechanism studies.展开更多
Low levels of environmental education,energy consumption,and other anthropogenic factors strongly contribute to the rising temperature in the world's atmosphere.As such,this study reveals how energy consumption an...Low levels of environmental education,energy consumption,and other anthropogenic factors strongly contribute to the rising temperature in the world's atmosphere.As such,this study reveals how energy consumption and education affect the ecological footprint(EF)and also determines the education thresholds for EF sustainability in sub-Saharan Africa(SSA).The estimation methods in this study are strictly second-generation econometric techniques because of the problems of slope heterogeneity and cross-sectional dependence discovered in the preliminary analysis.The results confirm cointegration,warranting the need for long-run parameter estimators.The Augment Mean Group estimator suggests that natural resources,non-renewable energy consumption(NRE),and economic growth increase the EF.Although renewable energy consumption(REN)and globalization reduce the EF,these indicators are insignificant.The results of the Method of Moment Quantile Regression(MMQR)reveal that REN exacts an indirect effect on the EF via education.Furthermore,the education thresholds required for ecological sustainability have been established.In line with these outcomes,it is proposed that the region redesign its energy policy to encourage eco-friendly consumption by leaning more on pro-environmental strategies and tightening environmental regulations.展开更多
文摘This paper aims to explore the application of Extreme Value Theory (EVT) in estimating the conditional extreme quantile for time-to-event outcomes by examining the functional relationship between ambulatory blood pressure trajectories and clinical outcomes in stroke patients. The study utilizes EVT to analyze the functional connection between ambulatory blood pressure trajectories and clinical outcomes in a sample of 297 stroke patients. The 24-hour ambulatory blood pressure measurement curves for every 15 minutes are considered, acknowledging a censored rate of 40%. The findings reveal that the sample mean excess function exhibits a positive gradient above a specific threshold, confirming the heavy-tailed distribution of data in stroke patients with a positive extreme value index. Consequently, the estimated conditional extreme quantile indicates that stroke patients with higher blood pressure measurements face an elevated risk of recurrent stroke occurrence at an early stage. This research contributes to the understanding of the relationship between ambulatory blood pressure and recurrent stroke, providing valuable insights for clinical considerations and potential interventions in stroke management.
文摘This paper presents a new class of test procedures for two-sample location problem based on subsample quantiles. The class includes Mann-Whitney test as a special case. The asymptotic normality of the class of tests proposed is established. The asymptotic relative performance of the proposed class of test with respect to the optimal member of Xie and Priebe (2000) is studied in terms of Pitman efficiency for various underlying distributions.
基金funded by the National Key R&D Program of China (2017YFD0600402)Provincial Funding for National Key R&D Program of China in Heilongjiang Province(Project No.GX18B041)the Overseas Famous Scholar Program of the Ministry of Educatoin,China (Project No.MS2016DBLY018)。
文摘Stochastic frontier analysis and quantile regression are the two econometric approaches that have been commonly adopted in the determination of the self-thinning boundary line or surface in two and higher dimensions since their introduction to the field some 20 years ago.However,the rational for using one method over the other has,in most cases,not been clearly explained perhaps due to a lack of adequate appreciation of differences between the two approaches for delineating the self-thinning surface.Without an adequate understanding of such differences,the most informative analysis may become a missed opportunity,leading to an inefficient use of data,weak statistical inferences and a failure to gain greater insight into the dynamics of plant populations and forest stands that would otherwise be obtained.Using data from 170 plot measurements in even-aged Larix olgensis(A.Henry) plantations across a wide range of site qualities and with different abundances of woody weeds,i.e.naturally regenerated non-crop species,in northeast China,this study compared the two methods in determining the self-thinning surface across eight sample sizes from 30 to 170 with an even interval of 20 observations and also over a range of quantiles through repeated random sampling and estimation.Across all sample sizes and over the quantile range of 0.90 ≤τ≤0.99,the normal-half normal stochastic frontier estimation proved to be superior to quantile regression in statistical efficiency.Its parameter estimates had lower degrees of variability and correspondingly narrower confidence intervals.This greater efficiency would naturally be conducive to making statistical inferences.The estimated self-thinning surface using all 170 observations enveloped about 96.5% of the data points,a degree of envelopment equivalent to a regression quantile estimation with aτ of 0.965.The stochastic frontier estimation was also more objective because it did not involve the subjective selection of a particular value of τ for the favoured self-thinning surface from several mutually intersecting surfaces as in quantile regression.However,quantile regression could still provide a valuable complement to stochastic frontier analysis in the estimation of the self-thinning surface as it allows the examination of the impact of variables other than stand density on different quantiles of stand biomass.
基金Supported by the National Natural Science Foundation of China(11271088,11361011,11201088)the Natural Science Foundation of Guangxi(2013GXNSFAA019004,2013GXNSFAA019007,2013GXNSFBA019001)
文摘In this paper, we obtain the joint empirical likelihood confidence regions for a finite number of quantiles under strong mixing samples. As an application of this result, the empirical likelihood confidence intervals for the difference of any two quantiles are also obtained.
文摘The large sample estimation of standard deviation of logistic distribution employs the asymptotically best linear unbiased estimators based on sample quantiles. The sample quantiles are established from a pair of single spacing. Finally, a table of the variances and efficiencies of the estimator for 5≤n≤65 is provided and comparison is made with other linear estimators.
文摘The minimum risk equivariant estimator of a quantile of the common marginal distribution in a multivariate Lomax distribution with unknown location and scale parameters under Linex loss function is considered.
文摘In this paper, the problem of nonparametric estimation of finite population quantile function using multiplicative bias correction technique is considered. A robust estimator of the finite population quantile function based on multiplicative bias correction is derived with the aid of a super population model. Most studies have concentrated on kernel smoothers in the estimation of regression functions. This technique has also been applied to various methods of non-parametric estimation of the finite population quantile already under review. A major problem with the use of nonparametric kernel-based regression over a finite interval, such as the estimation of finite population quantities, is bias at boundary points. By correcting the boundary problems associated with previous model-based estimators, the multiplicative bias corrected estimator produced better results in estimating the finite population quantile function. Furthermore, the asymptotic behavior of the proposed estimators </span><span style="font-family:Verdana;">is</span><span style="font-family:Verdana;"> presented</span><span style="font-family:Verdana;">. </span><span style="font-family:Verdana;">It is observed that the estimator is asymptotically unbiased and statistically consistent when certain conditions are satisfied. The simulation results show that the suggested estimator is quite well in terms of relative bias, mean squared error, and relative root mean error. As a result, the multiplicative bias corrected estimator is strongly suggested for survey sampling estimation of the finite population quantile function.
基金Project supported in part by a postdoctoral fellowship and the National Natural Science Foundation of China.
文摘The ordinary quantiles for univariate data were successfully generalized to linear modelsin Koenker and Bassett. Regression quantiles provide more specific and more global in-formation on the relationship of two variables through their distributions. Mosteller andTukey argued that the use of regression quantiles helps to provide a more complete pic-
基金Supported by NNSF of China(11501005,11671012,11701004,11701005)NSF of Anhui Province(1808085QA03,1808085QA17,1808085QF212)Provincial Natural Science Research Project of Anhui Colleges(KJ2017A027,KJ2018A0030)
文摘On the one hand,we investigate the Bahadur representation for sample quantiles underφ-mixing sequence withφ(n)=O(n^-3)and obtain a rate as O(n-3/4 log n),a.s.On the other hand,by relaxing the condition of mixing coefficients to∑∞n=1φ^1/2(n)<∞,a rate O(n^-1/2(log n)^1/2),a.s.,is also obtained.
基金partially supported by the National Natural Science Foundation of China(No.11861042)the Fundamental Research Funds for the Central Universitiesthe Research Funds of Renmin University of China(No.18XNL012)。
文摘In this article, we put forward a new approach to estimate multiple conditional regression quantiles simultaneously. Unlike the double summation method in most of the literatures, our proposed model allows continuous variety for the quantile level over(0,1). As a result, all the quantile curves can be obtained via a 2-dimensional surface simultaneously. Most importantly, the proposed minimizing criterion can be readily transformed to a linear programming problem. We use tensor product bi-linear quantile smoothing B-splines tofit it. The asymptotic property of the estimator is derived and a real data set is analyzed to demonstrate the proposed method.
基金Supported by the Natural Science Foundation of Fujian Province(2022J011177,2024J01903)the Key Project of Fujian Provincial Education Department(JZ230054)。
文摘In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This article considers subgroup analysis methods for longitudinal data containing multiple covariates and biomarkers.We divide subgroups based on whether a linear combination of these biomarkers exceeds a predetermined threshold,and assess the heterogeneity of treatment effects across subgroups using the interaction between subgroups and exposure variables.Quantile regression is used to better characterize the global distribution of the response variable and sparsity penalties are imposed to achieve variable selection of covariates and biomarkers.The effectiveness of our proposed methodology for both variable selection and parameter estimation is verified through random simulations.Finally,we demonstrate the application of this method by analyzing data from the PA.3 trial,further illustrating the practicality of the method proposed in this paper.
文摘Since its inception,the epsilon distribution has piqued the interest of statisticians.It has been successfully used to solve a variety of statistical problems.In this article,we propose to use the quadratic rank transmutation map mechanism to extend this distribution.This mechanism is not new;it was already used to improve the modeling capabilities of a number of existing distributions.For the original epsilon distribution,we expect the same benefits.As a result,we implement the transmuted epsilon distribution as a flexible three-parameter distribution with a bounded domain.We demonstrate its key features,focusing on the properties of its distributional mechanism and conducting quantile and moment analyses.Applications of the model are presented using two data sets.We also perform a regression analysis based on this distribution.
文摘As the world’s fourth most populous country,Indonesia presents challenges and opportunities for sustainable energy progress,offering a critical context to investigate green human development(GHD).This study uniquely contributes to the literature by employing the planetary pressures-adjusted human development index(PHDI)as an indicator of GHD,which integrates environmental impacts into human development.Using static and dynamic econometric methods,including the quantile regression and autoregressive distributed lag model,it explores the impacts of renewable and nonrenewable energy consumption on GHD.The findings demonstrate that renewable energy currently has a detrimental impact on GHD due to its limited adoption and high costs.Conversely,nonrenewable energy positively influences GHD,as it is the primary energy source in the country and is becoming more efficient at reducing emissions.However,the study finds that greater use of renewable energy reduces its adverse effects,suggesting that as renewable energy technologies become more cost-effective and widely implemented,their initial adverse effects could be mitigated,leading to improved long-term GHD outcomes.These findings carry important implications for Indonesia,where the govern‐ment is striving to expand renewable energy capacity while promoting equitable development across its archi‐pelagic regions.They underscore the critical role of energy policy in balancing economic,social,and environmental goals,contributing meaningfully to the country’s sustainable development agenda.
基金supported by the Young Scientists Fund of the National Key R&D Program of China(No.2022YFD2201800)the Youth Science Fund Program of National Natural Science Foundation of China(No.32301581)+2 种基金the Joint Funds for Regional Innovation and Development of the National Natural Science Foundation of China(No.U21A20244)the China Postdoctoral Science Foundation(No.2024M750383)the Heilongjiang Touyan Innovation Team Program(Technology Development Team for High-Efficiency Silviculture of Forest Resources).
文摘Branch size is a crucial characteristic,closely linked to both tree growth and wood quality.A review of existing branch size models reveals various approaches,but the ability to estimate branch diameter and length within the same whorl remains underexplored.In this study,a total of 77 trees were sampled from Northeast China to model the vertical distribution of branch diameter and length within each whorl along the crown.Several commonly used functions were taken as the alternative model forms,and the quantile regression method was employed and compared with the classical two-step modeling approach.The analysis incorporated stand,tree,and competition factors,with a particular focus on how these factors influence branches of varying sizes.The modified Weibull function was chosen as the optimal model,due to its excellent performance across all quantiles.Eight quantile regression curves(ranging from 0.20 to 0.85)were combined to predict branch diameter,while seven curves(ranging from 0.20 to 0.80)were used for branch length.The results showed that the quantile regression method outperformed the classical approach at model fitting and validation,likely due to its ability to estimate different rates of change across the entire branch size distribution.Lager branches in each whorl were more sensitive to changes in DBH,crown length(CL),crown ratio(CR)and dominant tree height(H_(dom)),while slenderness(HDR)more effectively influenced small and medium-sized branches.The effect of stand basal area(BAS)was relatively consistent across different branch sizes.The findings indicate that quantile regression is a good way not only a more accurate method for predicting branch size but also a valuable tool for understanding how branch growth responds to stand and tree factors.The models developed in this study are prepared to be further integrated into tree growth and yield simulation system,contributing to the assessment and promotion of wood quality.
基金Under the auspices of the National Natural Science Foundation of China(No.42271181,41871111)。
文摘Environmental inequality is a prevalent issue in developing countries undergoing urban expansion.Urban expansion induces the formation and evolution of environmental inequality by creating environmental and structural conditions that lead to the spatial relocation of environmental hazards and the socio-spatial segregation of different groups in developing countries.This study investigated the spatial patterns and temporal trends of environmental inequality under urban expansion in Guangzhou,a megacity in China.It considered how environmental disparities and socio-demographic attributes interact in terms of industrial pollution exposure using additive semiparametric quantile regression,combined with spatial visualisation,on the basis of the economic and population census data from 1990 to 2020.This study revealed that urban expansion sparked the spatial displacement of environmental risks and the social-spatial differentiation,exposing the peripheral regions and disadvantaged groups to higher environmental risks.A reciprocal transformation occurred between central and peripheral regions,as well as a process of redistributing environmental risks across social space.In the context of urban expansion in developing countries,the causes of environmental inequality shifted from individual socio-economic differences to structural factors,such as industrial layout and social division of labour in cities,leading to the spatial displacement and concealment of environmental inequality.This study provides insights and guidance for policymakers to address the issue of environmental inequality in the context of urban expansion.
文摘Agriculture has become the backbone of most developing countries in the world, especially Tubah Sub-Division North West region, Cameroon. Following the COVID-19 pandemic and socio-political crisis that hit Cameroon’s economy, there has been a steady increase in food insecurity, which has paved the way for farmers to adopt some sustainable strategies to boost agricultural productivity. Therefore, in trying to find models for survival and the pursuit of growth, farmers adopted some traditional farming methods and the use of local input as a means of sustainability. This study specifically seeks to analyze the effect of sustainability strategies on agricultural productivity in Tubah sub-division North West Region, Cameroon. The data was elicited via a survey questionnaire administered to 202 participating farmers selected from the different farmer organizations in the Tubah sub-division. Using cluster-sampling approach, proximity villages were grouped into four clusters of villages, and stratified sampling was used to select farmers to participate in the study. The objective of the study was achieved using OLS and quantile regression estimation techniques. The result showed evidence that the sustainability strategies implemented by the farmers decreased agricultural productivity in the 25th quantile, and at the 50th and 75th quantile, agricultural productivity still declined. This decline is because of unsustainable agricultural strategies like the use of slash and burn, the use of chemical fertilizers, inadequate capital, low level of education, inadequate farming experience, inadequate income, inadequate farm size, and the type of technology used for farming. Based on the findings, this study recommends that the government should organize training programs and seminars, subsidize farm inputs, grant agricultural loans to farmers, and initiate and support mechanized agriculture to boost agricultural productivity.
文摘Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread application, often encounter difficulties in handling the complexities of oil production data, which is characterized by non-linear patterns, skewed distributions, and the presence of outliers. To overcome these limitations, deep learning methods have emerged as more robust alternatives. However, while deep neural networks offer improved accuracy, they demand substantial amounts of data for effective training. Conversely, shallow networks with fewer layers lack the capacity to model complex data distributions adequately. To address these challenges, this study introduces a novel hybrid model called Transfer LSTM to GRU (TLTG), which combines the strengths of deep and shallow networks using transfer learning. The TLTG model integrates Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU) to enhance predictive accuracy while maintaining computational efficiency. Gaussian transformation is applied to the input data to reduce outliers and skewness, creating a more normal-like distribution. The proposed approach is validated on datasets from various wells in the Tahe oil field, China. Experimental results highlight the superior performance of the TLTG model, achieving 100% accuracy and faster prediction times (200 s) compared to eight other approaches, demonstrating its effectiveness and efficiency.
基金Under the auspices of National Key Research and Development Program(No.2023YFC3804001)National Natural Science Foundation of China(No.42201440)。
文摘Understanding the urban-rural development mechanism is critical for implementing rural revival and new-type urbanization.However,it remains a challenge to quantify the urban-rural integrated development level(URIDL)and its impact factors.Hence,we constructed an assessment system for the URIDL from spatial,economic,social,life,and ecological integration.The spatial autocorrelation and Spearman rank correlation coefficients were used to assess the spatiotemporal variation of the URIDL and the trade-off synergistic relationship among the subsystems at the provincial scale in China using socio-economic statistical data from 2000 to 2020.A spatial panel quantile regression model was used to analyze the driving mechanism.The results showed that the URIDL of China increased by 0.19%from 2000 to 2020,and a high-high(H-H)spatial agglomeration pattern occurred in the Yangtze River Delta and the Beijing-Tianjin-Hebei regions.Spatial integration significantly contributed to the other subsystems,whereas economic integration had a significant negative impact on the other subsystems in the eastern coastal and southwestern regions.Per capita Gross Domestic Product(GDP)improved the URIDL,whereas other factors,such as fiscal revenue decentralization,had inhibiting effects.Notably,the impact of factors on URIDL varies across different quantiles.Finally,we proposed policy recommendations for differentiated improvement of URIDL based on its evolution and regional development level during the research period.
基金supported by the National Natural Science Foundation of China(No.21976050)the Science and Technology Program of Hebei Province(No.21377779D)+3 种基金the Natural Science Foundation of Hebei Province(No.B2020206008)China Postdoctoral Science Foundation(Nos.2023M730317 and 2023T160066)the Fundamental Research Funds for the Central Universities(No.3332023042)the Open Project of Hebei Key Laboratory of Environment and Human Health(No.202301).
文摘Previous studies have suggested that abnormal hepatobiliary system function may contribute to poor prognosis in patientswith acute coronary syndrome(ACS)and that abnormal hepatobiliary system function may be associated with per-and polyfluoroalkyl substances(PFAS)exposure.However,there is limited evidence for this association in cardiovascular subpopulations,particularly in the ACS patients.Therefore,we performed this study to evaluate the association between plasma PFAS exposure and hepatobiliary system function biomarkers in patients with ACS.This study included 546 newly diagnosed ACS patients at the Second Hospital of Hebei Medical University,and data on 15 hepatobiliary system function biomarkers were obtained from medical records.Associations between single PFAS and hepatobiliary system function biomarkers were assessed using multiple linear regression models and restricted cubic spline model(RCS),and mixture effects were assessed using the Quantile g-computation model.The results showed that total bile acids(TBA)was negative associated with perfluorohexane sulfonic acid(PFHxS)(-7.69%,95%CI:-12.15%,-3.01%).According to the RCS model,linear associations were found between TBA and PFHxS(P for overall=0.003,P for non-linear=0.234).We also have observed the association between between PFAS congeners and liver enzyme such as aspartate aminotransferase(AST)and α-l-Fucosidase(AFU),but it was not statistically significant after correction.In addition,Our results also revealed an association between prealbumin(PA)and PFAS congeners as well as mixtures.Our findings have provided a piece of epidemiological evidence on associations between PFAS congeners or mixture,and serum hepatobiliary system function biomarkers in ACS patients,which could be a basis for subsequent mechanism studies.
文摘Low levels of environmental education,energy consumption,and other anthropogenic factors strongly contribute to the rising temperature in the world's atmosphere.As such,this study reveals how energy consumption and education affect the ecological footprint(EF)and also determines the education thresholds for EF sustainability in sub-Saharan Africa(SSA).The estimation methods in this study are strictly second-generation econometric techniques because of the problems of slope heterogeneity and cross-sectional dependence discovered in the preliminary analysis.The results confirm cointegration,warranting the need for long-run parameter estimators.The Augment Mean Group estimator suggests that natural resources,non-renewable energy consumption(NRE),and economic growth increase the EF.Although renewable energy consumption(REN)and globalization reduce the EF,these indicators are insignificant.The results of the Method of Moment Quantile Regression(MMQR)reveal that REN exacts an indirect effect on the EF via education.Furthermore,the education thresholds required for ecological sustainability have been established.In line with these outcomes,it is proposed that the region redesign its energy policy to encourage eco-friendly consumption by leaning more on pro-environmental strategies and tightening environmental regulations.
