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
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
Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantil...Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.展开更多
Purpose–This study aimed to facilitate a rapid evaluation of track service status and vehicle ride comfort based on car body acceleration.Consequently,a low-cost,data-driven approach was proposed for analyzing speed-...Purpose–This study aimed to facilitate a rapid evaluation of track service status and vehicle ride comfort based on car body acceleration.Consequently,a low-cost,data-driven approach was proposed for analyzing speed-related acceleration limits in metro systems.Design/methodology/approach–A portable sensing terminal was developed to realize easy and efficient detection of car body acceleration.Further,field measurements were performed on a 51.95-km metro line.Data from 272 metro sections were tested as a case study,and a quantile regression method was proposed to fit the control limits of the car body acceleration at different speeds using the measured data.Findings–First,the frequency statistics of the measured data in the speed-acceleration dimension indicated that the car body acceleration was primarily concentrated within the constant speed stage,particularly at speeds of 15.4,18.3,and 20.9 m/s.Second,resampling was performed according to the probability density distribution of car body acceleration for different speed domains to achieve data balance.Finally,combined with the traditional linear relationship between speed and acceleration,the statistical relationships between the speed and car body acceleration under different quantiles were determined.We concluded the lateral/vertical quantiles of 0.8989/0.9895,0.9942/0.997,and 0.9998/0.993 as being excellent,good,and qualified control limits,respectively,for the lateral and vertical acceleration of the car body.In addition,regression lines for the speedrelated acceleration limits at other quantiles(0.5,0.75,2s,and 3s)were obtained.Originality/value–The proposed method is expected to serve as a reference for further studies on speedrelated acceleration limits in rail transit systems.展开更多
Value at risk(VaR)and expected shortfall(ES)have emerged as standard measures for detecting the market risk of financial assets and play essential roles in investment decisions,external regulations,and risk capital al...Value at risk(VaR)and expected shortfall(ES)have emerged as standard measures for detecting the market risk of financial assets and play essential roles in investment decisions,external regulations,and risk capital allocation.However,existing VaR estimation approaches fail to accurately reflect downside risks,and the ES estimation technique is quite limited owing to its challenging implementation.This causes financial institutions to overestimate or underestimate investment risk and finally leads to the inefficient allocation of financial resources.The main purpose of this study is to use machine learning to improve the accuracy of VaR estimation and provide an effective tool for ES estimation.Specifically,this study proposes a VaR estimator by combining quantile regression with“Mogrifier”recurrent neural networks to capture the“long memory”and“clustering”properties of financial assets;while for estimating ES,this study directly models the quantile of assets and employs generative adversarial networks to generate future tail risk scenarios.In addition to the typical properties of financial assets,the model design is also consistent with heterogeneous market theory.An empirical application to four major global stock indices shows that our model is superior to other existing models.展开更多
This paper studies how the price movements of pork,chicken and egg respond to those of related cost factors in short terms in Chinese market.We employ a linear quantile approach not only to explore potential data hete...This paper studies how the price movements of pork,chicken and egg respond to those of related cost factors in short terms in Chinese market.We employ a linear quantile approach not only to explore potential data heteroscedasticity but also to generate confidence bands for the purpose of price stability study.We then evaluate our models by comparing the prediction intervals generated from the quantile regression models with in-sample and out-of-sample forecasts.Using monthly data from January 2000 to October 2010,we observed these findings：（i） the price changes of cost factors asymmetrically and unequally influence those of the livestock across different quantiles;（ii） the performance of our models is robust and consistent for both in-sample and out-of-sample forecasts;（iii） the confidence intervals generated from 0.05th and 0.95th quantile regression models are good methods to forecast livestock price fluctuation.展开更多
Quantile regression（QR） is proposed to examine the relationships between large-scale atmospheric variables and all parts of the distribution of daily precipitation amount at Beijing Station from 1960 to 2008. QR is ...Quantile regression（QR） is proposed to examine the relationships between large-scale atmospheric variables and all parts of the distribution of daily precipitation amount at Beijing Station from 1960 to 2008. QR is also applied to evaluate the relationship between large-scale predictors and extreme precipitation（90th quantile） at 238 stations in northern China.Finally, QR is used to fit observed daily precipitation amounts for wet days at four sample stations. Results show that meridional wind and specific humidity at both 850 h Pa and 500 h Pa（V850, SH850, V500, and SH500） strongly affect all parts of the Beijing precipitation distribution during the wet season（April–September）. Meridional wind, zonal wind, and specific humidity at only 850 h Pa（V850, U850, SH850） are significantly related to the precipitation distribution in the dry season（October–March）. Impacts of these large-scale predictors on the daily precipitation amount with higher quantile become stronger, whereas their impact on light precipitation is negligible. In addition, SH850 has a strong relationship with wet-season extreme precipitation across the entire region, whereas the impacts of V850, V500, and SH500 are mainly in semi-arid and semi-humid areas. For the dry season, both SH850 and V850 are the major predictors of extreme precipitation in the entire region. Moreover, QR can satisfactorily simulate the daily precipitation amount at each station and for each season, if an optimum distribution family is selected. Therefore, QR is valuable for detecting the relationship between the large-scale predictors and the daily precipitation amount.展开更多
Genomic prediction(GP)has become a valuable tool for predicting the performance of selection candidates for the next breeding cycle.A vast majority of statistical linear models on which GP is based rely on the assumpt...Genomic prediction(GP)has become a valuable tool for predicting the performance of selection candidates for the next breeding cycle.A vast majority of statistical linear models on which GP is based rely on the assumption of normality of the residuals and therefore on the response variable itself.In this study,we propose to use Bayesian regularized quantile regression(BRQR)in the context of GP;the model has been successfully used in other research areas.We evaluated the prediction ability of the proposed model and compared it with the Bayesian ridge regression(BRR;equivalent to genomic best linear unbiased predictor,GBLUP).In addition,BLUP can be used with pedigree information obtained from the coefficient of coancestry(ABLUP).We have found that the prediction ability of BRQR is comparable to that of BRR and,in some cases,better;it also has the potential to efficiently deal with outliers.A program written in the R statistical package is available as Supplementary material.展开更多
Scour has been widely accepted as a key reason for bridge failures.Bridges are susceptible and sensitive to the scour phenomenon,which describes the loss of riverbed sediments around the bridge supports because of flo...Scour has been widely accepted as a key reason for bridge failures.Bridges are susceptible and sensitive to the scour phenomenon,which describes the loss of riverbed sediments around the bridge supports because of flow.The carrying capacity of a deep-water foundation is influenced by the formation of a scour hole,which means that a severe scour can lead to a bridge failure without warning.Most of the current scour predictions are based on deterministic models,while other loads at bridges are usually provided as probabilistic values.To integrate scour factors with other loads in bridge design and research,a quantile regression model was utilized to estimate scour depth.Field data and experimental data from previous studies were collected to build the model.Moreover,scour estimations using the HEC-18 equation and the proposed method were compared.By using the“CCC(Calculate,Confirm,and Check)”procedure,the probabilistic concept could be used to calculate various scour depths with the targeted likelihood according to a specified chance of bridge failure.The study shows that with a sufficiently large and continuously updated database,the proposed model could present reasonable results and provide guidance for scour mitigation.展开更多
The lack of covariate data is one of the hotspots of modern statistical analysis.It often appears in surveys or interviews,and becomes more complex in the presence of heavy tailed,skewed,and heteroscedastic data.In th...The lack of covariate data is one of the hotspots of modern statistical analysis.It often appears in surveys or interviews,and becomes more complex in the presence of heavy tailed,skewed,and heteroscedastic data.In this sense,a robust quantile regression method is more concerned.This paper presents an inverse weighted quantile regression method to explore the relationship between response and covariates.This method has several advantages over the naive estimator.On the one hand,it uses all available data and the missing covariates are allowed to be heavily correlated with the response;on the other hand,the estimator is uniform and asymptotically normal at all quantile levels.The effectiveness of this method is verified by simulation.Finally,in order to illustrate the effectiveness of this method,we extend it to the more general case,multivariate case and nonparametric case.展开更多
An airborne multi-spectral camera was used in this study to estimate rice yields.The experimental data were achieved by obtaining a multi-spectral image of the rice canopy in an experimental field throughout the joint...An airborne multi-spectral camera was used in this study to estimate rice yields.The experimental data were achieved by obtaining a multi-spectral image of the rice canopy in an experimental field throughout the jointing stage(July,2017)and extracting five vegetation indices.Vegetation indices and rice growth parameter data were compared and analyzed.Effective predictors were screened by using significance analysis and quantile and ordinary least square(OLS)regression models estimating rice yields were constructed.The results showed that a quantile regression model based on normalized difference vegetation indices(NDVI)and rice yields performed was best forτ=0.7 quantile.Thus,NDVI was determined as an effective variable for the rice yield estimation during the jointing stage.The accuracy of the quantile regression estimation model was then assessed using RMES and MAPE test indicators.The yields by this approach had better results than those of an OLS regression estimation model and showed that quantile regression had practical applications and research significance in rice yields estimation.展开更多
Based on the investigation data of social position of national women in the third phase by National Women's Federation and National Bureau of Statistics in 2010,regression analysis on sex wage difference is conduc...Based on the investigation data of social position of national women in the third phase by National Women's Federation and National Bureau of Statistics in 2010,regression analysis on sex wage difference is conducted. It is divided into two parts. The first part is the impact on wage by sex,and it is divided into whole country,eastern,central and western regions. The second part is the impact on wage by different education backgrounds. It tries to explore sex wage difference situation at different positions of wage distribution,study if there exists " ceiling effect" or " floor effect" in population's wage distribution situation,sex wage difference situation in eastern,central and western regions and the education's impact on future income situations of men and women.展开更多
Climate change is described as a potentially catastrophic phenomenon with the capacity to disrupt agricultural production, economies, health systems, education, and infrastructure, among other systems. In Florida, cli...Climate change is described as a potentially catastrophic phenomenon with the capacity to disrupt agricultural production, economies, health systems, education, and infrastructure, among other systems. In Florida, climate change is a concern because of the state’s extensive coastline and its influence on the economy, as well as residents’ safety and well-being. As early as 2007, researchers forecasted that vulnerable wetlands, mangroves, fisheries, and coastal infrastructure in Florida may be significantly damaged or destroyed by 2060. Climate change communication (CCC) is described as a complex problem that requires several layers of attention, especially in achieving the desired outcome of behavior change. Previous research suggested that climate change communicators would be more effective if they understood their audiences and their communication capacities. The purpose of the study was to determine the impact of demographic factors on social communication for residents of Florida. A survey was used to collect the data through an address-based sampling (ABS) method, where a total of 318 usable responses were received from Florida residence 18 years or older. A latent construct for describing social communication (Social Communication Index [SCI]) was created as the dependent variable and was tested against eight variables using a quantile regression approach. Using quantiles in 0.1 intervals, the results showed that knowledge, age, income, newspaper use, urbanicity, and race affected the SCI in one or more quantiles. Social media, sex, and religiosity were insignificant throughout all quantiles. While most of the results align with previous research, there is the need for further probing into social communication on climate change to ensure that audience segments are provided with climate change information through the channels they primarily use.展开更多
In this paper, three smoothed empirical log-likelihood ratio functions for the parameters of nonlinear models with missing response are suggested. Under some regular conditions, the corresponding Wilks phenomena are o...In this paper, three smoothed empirical log-likelihood ratio functions for the parameters of nonlinear models with missing response are suggested. Under some regular conditions, the corresponding Wilks phenomena are obtained and the confidence regions for the parameter can be constructed easily.展开更多
In various fields such as medical science and finance,it is not uncommon that the data are heavy-tailed and/or not fully observed,calling for robust inference methods that can deal with the outliers and incompleteness...In various fields such as medical science and finance,it is not uncommon that the data are heavy-tailed and/or not fully observed,calling for robust inference methods that can deal with the outliers and incompleteness efficiently.In this paper,the authors propose a rank score test for quantile regression with fixed censored responses,based on the standard quantile regression in an informative subset which is computationally efficient and robust.The authors further select the informative subset by the multiply robust propensity scores,and then derive the asymptotic properties of the proposed test statistic under both the null and local alternatives.Moreover,the authors conduct extensive simulations to verify the validity of the proposed test,and apply it to a human immunodeficiency virus data set to identify the important predictors for the conditional quantiles of the censored viral load.展开更多
Current methods used in genome-wide association studies frequently lack power owing to their inability to detect heterogeneous associations and rare and multiallelic variants.To address these issues,quantile regressio...Current methods used in genome-wide association studies frequently lack power owing to their inability to detect heterogeneous associations and rare and multiallelic variants.To address these issues,quantile regression is integrated with a three(compressed)variance component multi-locus random-SNP-effect mixed linear model(3VmrMLM)to propose q3VmrMLM for detecting heterogeneous quantitative trait nucleotides(QTNs)and QTN-by-environment interactions(QEIs),and then design haplotype-based q3VmrMLM(q3VmrMLM-Hap)for identifying multiallelic haplotypes and rare variants.In Monte Carlo simulation studies,q3VmrMLM had higher power than 3VmrMLM,sequence kernel association test(SKAT),and integrated quantile rank test(iQRAT).In a re-analysis of 10 traits in 1439 rice hybrids,261 known genes were identified only by q3VmrMLM and q3VmrMLM-Hap,whereas 175 known genes were detected by both the new and existing methods.Of all the significant QTNs with known genes,q3VmrMLM(179:140 variance heterogeneity and 157 quantile effect heterogeneity)found more heterogeneous QTNs than 3VmrMLM(123),SKAT(27),and iQRAT(29);q3VmrMLM-Hap(121)mapped more lowfrequency(<0.05)QTNs than q3VmrMLM(51),3VmrMLM(43),SKAT(11),and iQRAT(12);and q3VmrMLM-Hap(12),q3VmrMLM(16),and 3VmrMLM(12)had similar power in identifying gene-by-environment interactions.All significant and suggested QTNs achieved the highest predictive accuracy(r=0.9045).In conclusion,this study describes a new and complementary approach to mining genes and unraveling the genetic architecture of complex traits in crops.展开更多
Quantile regression is widely used in variable relationship research for statistical learning.Traditional quantile regression model is based on vector-valued covariates and can be efficiently estimated via traditional...Quantile regression is widely used in variable relationship research for statistical learning.Traditional quantile regression model is based on vector-valued covariates and can be efficiently estimated via traditional estimation methods.However,many modern applications involve tensor data with the intrinsic tensor structure.Traditional quantile regression can not deal with tensor regression issues well.To this end,we consider a tensor quantile regression with tensor-valued covariates and develop a novel variational Bayesian estimation approach to make estimation and prediction based on the asymmetric Laplace model and the CANDECOMP/PARAFAC decomposition of tensor coefficients.To incorporate the sparsity of tensor coefficients,we consider the multiway shrinkage priors for marginal factor vectors of tensor coefficients.The key idea of the proposed method is to efficiently combine the prior structural information of tensor and utilize the matricization of tensor decomposition to simplify the complexity of tensor coefficient estimation.The coordinate ascent algorithm is employed to optimize variational lower bound.Simulation studies and a real example show the numerical performances of the proposed method.展开更多
In this paper,we focus on the problem of nonparametric quantile regression with left-truncated and right-censored data.Based on Nadaraya-Watson(NW)Kernel smoother and the technique of local linear(LL)smoother,we const...In this paper,we focus on the problem of nonparametric quantile regression with left-truncated and right-censored data.Based on Nadaraya-Watson(NW)Kernel smoother and the technique of local linear(LL)smoother,we construct the NW and LL estimators of the conditional quantile.Under strong mixing assumptions,we establish asymptotic representation and asymptotic normality of the estimators.Finite sample behavior of the estimators is investigated via simulation,and a real data example is used to illustrate the application of the proposed methods.展开更多
Sudden reductions in crop yield(i.e.,yield shocks)severely disrupt the food supply,intensify food insecurity,depress farmers'welfare,and worsen a country's economic conditions.Here,we study the spatiotemporal ...Sudden reductions in crop yield(i.e.,yield shocks)severely disrupt the food supply,intensify food insecurity,depress farmers'welfare,and worsen a country's economic conditions.Here,we study the spatiotemporal patterns of wheat yield shocks,quantified by the lower quantiles of yield fluctuations,in 86 countries over 30 years.Furthermore,we assess the relationships between shocks and their key ecological and socioeconomic drivers using quantile regression based on statistical(linear quantile mixed model)and machine learning(quantile random forest)models.Using a panel dataset that captures spatiotemporal patterns of yield shocks and possible drivers in 86 countries,we find that the severity of yield shocks has been increasing globally since 1997.Moreover,our cross-validation exercise shows that quantile random forest outperforms the linear quantile regression model.Despite this performance difference,both models consistently reveal that the severity of shocks is associated with higher weather stress,nitrogen fertilizer application rate,and gross domestic product(GDP)per capita(a typical indicator for economic and technological advancement in a country).While the unexpected negative association between more severe wheat yield shocks and higher fertilizer application rate and GDP per capita does not imply a direct causal effect,they indicate that the advancement in wheat production has been primarily on achieving higher yields and less on lowering the possibility and magnitude of sharp yield reductions.Hence,in the context of growing extreme weather stress,there is a critical need to enhance the technology and management practices that mitigate yield shocks to improve the resilience of the world food systems.展开更多
A comprehensive evaluation index system is constructed,and the entropy weight TOPSIS method isused to measure the optimization level of the digital economy and tourism industry structure of 30 provinces inChina from 2...A comprehensive evaluation index system is constructed,and the entropy weight TOPSIS method isused to measure the optimization level of the digital economy and tourism industry structure of 30 provinces inChina from 2012 to 2021.Moreover,models such as quantile regression and panel threshold are used to explorethe influence of the digital economy(DIG)on the optimization of the tourism industry structure(TIS)as well as itstransmission mechanism.The study reveals that DIG significantly promotes TIS,which remains valid after endogeneity and robustness tests;the impact of DIG on TIS exhibited a“U-shape”effect that first decreases and thenincreases,and its highest significance is at the 90%quartile level.Threshold model tests revealed a nonlinearthreshold effect with DIG and tourism total factor productivity(TTFP)as a single threshold and tourism technological progress index(TECH)as a double threshold,and the second threshold has the largest effect of 0.163.Mechanism analysis found that the mediating impact of the DIG on the TIS was mediated by increasing the TTFP,and the TECH accounted for the highest proportion of 12.15%.Regional analysis revealed that the role of DIG onthe TIS is Central>East>West>Northeast,and the empowering effect is more significant in the high digital economylevel area and the high tourism industry structure optimization area.展开更多
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.
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supported by the National Natural Science Foundation of China (Project No.42375192)the China Meteorological Administration Climate Change Special Program (CMA-CCSP+1 种基金Project No.QBZ202315)support by the Vector Stiftung through the Young Investigator Group"Artificial Intelligence for Probabilistic Weather Forecasting."
文摘Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.
基金the National Natural Science Foundation of China(NSFC)under No.52308473the National KeyR&DProgram under No.2022YFB2603301the China Postdoctoral Science Foundation funded project(Certificate Number:2023M743895).
文摘Purpose–This study aimed to facilitate a rapid evaluation of track service status and vehicle ride comfort based on car body acceleration.Consequently,a low-cost,data-driven approach was proposed for analyzing speed-related acceleration limits in metro systems.Design/methodology/approach–A portable sensing terminal was developed to realize easy and efficient detection of car body acceleration.Further,field measurements were performed on a 51.95-km metro line.Data from 272 metro sections were tested as a case study,and a quantile regression method was proposed to fit the control limits of the car body acceleration at different speeds using the measured data.Findings–First,the frequency statistics of the measured data in the speed-acceleration dimension indicated that the car body acceleration was primarily concentrated within the constant speed stage,particularly at speeds of 15.4,18.3,and 20.9 m/s.Second,resampling was performed according to the probability density distribution of car body acceleration for different speed domains to achieve data balance.Finally,combined with the traditional linear relationship between speed and acceleration,the statistical relationships between the speed and car body acceleration under different quantiles were determined.We concluded the lateral/vertical quantiles of 0.8989/0.9895,0.9942/0.997,and 0.9998/0.993 as being excellent,good,and qualified control limits,respectively,for the lateral and vertical acceleration of the car body.In addition,regression lines for the speedrelated acceleration limits at other quantiles(0.5,0.75,2s,and 3s)were obtained.Originality/value–The proposed method is expected to serve as a reference for further studies on speedrelated acceleration limits in rail transit systems.
基金supported by the Jiangxi Provincial Natural Science Foundation(20212ACB211003)the National Natural Science Foundation of China(No.71671029).
文摘Value at risk(VaR)and expected shortfall(ES)have emerged as standard measures for detecting the market risk of financial assets and play essential roles in investment decisions,external regulations,and risk capital allocation.However,existing VaR estimation approaches fail to accurately reflect downside risks,and the ES estimation technique is quite limited owing to its challenging implementation.This causes financial institutions to overestimate or underestimate investment risk and finally leads to the inefficient allocation of financial resources.The main purpose of this study is to use machine learning to improve the accuracy of VaR estimation and provide an effective tool for ES estimation.Specifically,this study proposes a VaR estimator by combining quantile regression with“Mogrifier”recurrent neural networks to capture the“long memory”and“clustering”properties of financial assets;while for estimating ES,this study directly models the quantile of assets and employs generative adversarial networks to generate future tail risk scenarios.In addition to the typical properties of financial assets,the model design is also consistent with heterogeneous market theory.An empirical application to four major global stock indices shows that our model is superior to other existing models.
基金supported by the Key Project of National Key Technology R&D Program of China(2009BADA9B01)
文摘This paper studies how the price movements of pork,chicken and egg respond to those of related cost factors in short terms in Chinese market.We employ a linear quantile approach not only to explore potential data heteroscedasticity but also to generate confidence bands for the purpose of price stability study.We then evaluate our models by comparing the prediction intervals generated from the quantile regression models with in-sample and out-of-sample forecasts.Using monthly data from January 2000 to October 2010,we observed these findings：（i） the price changes of cost factors asymmetrically and unequally influence those of the livestock across different quantiles;（ii） the performance of our models is robust and consistent for both in-sample and out-of-sample forecasts;（iii） the confidence intervals generated from 0.05th and 0.95th quantile regression models are good methods to forecast livestock price fluctuation.
基金jointly sponsored by the National Basic Research Program of China "973" Program (Grant No. 2012CB956203)the Knowledge Innovation Project (Grant No. KZCX2-EW-202)the National Natural Science Foundation of China (Grant Nos. 91325108 and 51339004)
文摘Quantile regression（QR） is proposed to examine the relationships between large-scale atmospheric variables and all parts of the distribution of daily precipitation amount at Beijing Station from 1960 to 2008. QR is also applied to evaluate the relationship between large-scale predictors and extreme precipitation（90th quantile） at 238 stations in northern China.Finally, QR is used to fit observed daily precipitation amounts for wet days at four sample stations. Results show that meridional wind and specific humidity at both 850 h Pa and 500 h Pa（V850, SH850, V500, and SH500） strongly affect all parts of the Beijing precipitation distribution during the wet season（April–September）. Meridional wind, zonal wind, and specific humidity at only 850 h Pa（V850, U850, SH850） are significantly related to the precipitation distribution in the dry season（October–March）. Impacts of these large-scale predictors on the daily precipitation amount with higher quantile become stronger, whereas their impact on light precipitation is negligible. In addition, SH850 has a strong relationship with wet-season extreme precipitation across the entire region, whereas the impacts of V850, V500, and SH500 are mainly in semi-arid and semi-humid areas. For the dry season, both SH850 and V850 are the major predictors of extreme precipitation in the entire region. Moreover, QR can satisfactorily simulate the daily precipitation amount at each station and for each season, if an optimum distribution family is selected. Therefore, QR is valuable for detecting the relationship between the large-scale predictors and the daily precipitation amount.
基金The maize and wheat data set used in this study comes from the Drought Tolerance Maize for Africa Project and from CIMMYT's Global Wheat Program.We are thankful to everyone who generated the data used in this article.
文摘Genomic prediction(GP)has become a valuable tool for predicting the performance of selection candidates for the next breeding cycle.A vast majority of statistical linear models on which GP is based rely on the assumption of normality of the residuals and therefore on the response variable itself.In this study,we propose to use Bayesian regularized quantile regression(BRQR)in the context of GP;the model has been successfully used in other research areas.We evaluated the prediction ability of the proposed model and compared it with the Bayesian ridge regression(BRR;equivalent to genomic best linear unbiased predictor,GBLUP).In addition,BLUP can be used with pedigree information obtained from the coefficient of coancestry(ABLUP).We have found that the prediction ability of BRQR is comparable to that of BRR and,in some cases,better;it also has the potential to efficiently deal with outliers.A program written in the R statistical package is available as Supplementary material.
基金Sponsored by the National Natural Science Foundation of China(Grant Nos.51908421 and 41172246).
文摘Scour has been widely accepted as a key reason for bridge failures.Bridges are susceptible and sensitive to the scour phenomenon,which describes the loss of riverbed sediments around the bridge supports because of flow.The carrying capacity of a deep-water foundation is influenced by the formation of a scour hole,which means that a severe scour can lead to a bridge failure without warning.Most of the current scour predictions are based on deterministic models,while other loads at bridges are usually provided as probabilistic values.To integrate scour factors with other loads in bridge design and research,a quantile regression model was utilized to estimate scour depth.Field data and experimental data from previous studies were collected to build the model.Moreover,scour estimations using the HEC-18 equation and the proposed method were compared.By using the“CCC(Calculate,Confirm,and Check)”procedure,the probabilistic concept could be used to calculate various scour depths with the targeted likelihood according to a specified chance of bridge failure.The study shows that with a sufficiently large and continuously updated database,the proposed model could present reasonable results and provide guidance for scour mitigation.
基金Supported by the National Natural Science Foundation of China(Grant No.11861042)the China Statistical Research Project(Grant No.2020LZ25)。
文摘The lack of covariate data is one of the hotspots of modern statistical analysis.It often appears in surveys or interviews,and becomes more complex in the presence of heavy tailed,skewed,and heteroscedastic data.In this sense,a robust quantile regression method is more concerned.This paper presents an inverse weighted quantile regression method to explore the relationship between response and covariates.This method has several advantages over the naive estimator.On the one hand,it uses all available data and the missing covariates are allowed to be heavily correlated with the response;on the other hand,the estimator is uniform and asymptotically normal at all quantile levels.The effectiveness of this method is verified by simulation.Finally,in order to illustrate the effectiveness of this method,we extend it to the more general case,multivariate case and nonparametric case.
基金Supported by the National Key R&D Program of China(2016YFD020060305)。
文摘An airborne multi-spectral camera was used in this study to estimate rice yields.The experimental data were achieved by obtaining a multi-spectral image of the rice canopy in an experimental field throughout the jointing stage(July,2017)and extracting five vegetation indices.Vegetation indices and rice growth parameter data were compared and analyzed.Effective predictors were screened by using significance analysis and quantile and ordinary least square(OLS)regression models estimating rice yields were constructed.The results showed that a quantile regression model based on normalized difference vegetation indices(NDVI)and rice yields performed was best forτ=0.7 quantile.Thus,NDVI was determined as an effective variable for the rice yield estimation during the jointing stage.The accuracy of the quantile regression estimation model was then assessed using RMES and MAPE test indicators.The yields by this approach had better results than those of an OLS regression estimation model and showed that quantile regression had practical applications and research significance in rice yields estimation.
文摘Based on the investigation data of social position of national women in the third phase by National Women's Federation and National Bureau of Statistics in 2010,regression analysis on sex wage difference is conducted. It is divided into two parts. The first part is the impact on wage by sex,and it is divided into whole country,eastern,central and western regions. The second part is the impact on wage by different education backgrounds. It tries to explore sex wage difference situation at different positions of wage distribution,study if there exists " ceiling effect" or " floor effect" in population's wage distribution situation,sex wage difference situation in eastern,central and western regions and the education's impact on future income situations of men and women.
文摘Climate change is described as a potentially catastrophic phenomenon with the capacity to disrupt agricultural production, economies, health systems, education, and infrastructure, among other systems. In Florida, climate change is a concern because of the state’s extensive coastline and its influence on the economy, as well as residents’ safety and well-being. As early as 2007, researchers forecasted that vulnerable wetlands, mangroves, fisheries, and coastal infrastructure in Florida may be significantly damaged or destroyed by 2060. Climate change communication (CCC) is described as a complex problem that requires several layers of attention, especially in achieving the desired outcome of behavior change. Previous research suggested that climate change communicators would be more effective if they understood their audiences and their communication capacities. The purpose of the study was to determine the impact of demographic factors on social communication for residents of Florida. A survey was used to collect the data through an address-based sampling (ABS) method, where a total of 318 usable responses were received from Florida residence 18 years or older. A latent construct for describing social communication (Social Communication Index [SCI]) was created as the dependent variable and was tested against eight variables using a quantile regression approach. Using quantiles in 0.1 intervals, the results showed that knowledge, age, income, newspaper use, urbanicity, and race affected the SCI in one or more quantiles. Social media, sex, and religiosity were insignificant throughout all quantiles. While most of the results align with previous research, there is the need for further probing into social communication on climate change to ensure that audience segments are provided with climate change information through the channels they primarily use.
文摘In this paper, three smoothed empirical log-likelihood ratio functions for the parameters of nonlinear models with missing response are suggested. Under some regular conditions, the corresponding Wilks phenomena are obtained and the confidence regions for the parameter can be constructed easily.
基金supported by the National Natural Science Foundation of China under Grant Nos.12171310 and 12371272the Shanghai“Project Dawn 2022”under Grant No.22SG52+5 种基金the Basic Research Project of Shanghai Science and Technology Commission under Grant No.22JC1400800the National Natural Science Foundation of China under Grant No.12371265the Shanghai National Foundation of Science under Grant No.21ZR1420700the Fundamental Research Funds for the Central Universities under Grant No.2022QKT001the General Research Fund of Hong Kong under Grant Nos.HKBU12303421 and HKBU12300123the National Natural Science Foundation of China under Grant No.12071305。
文摘In various fields such as medical science and finance,it is not uncommon that the data are heavy-tailed and/or not fully observed,calling for robust inference methods that can deal with the outliers and incompleteness efficiently.In this paper,the authors propose a rank score test for quantile regression with fixed censored responses,based on the standard quantile regression in an informative subset which is computationally efficient and robust.The authors further select the informative subset by the multiply robust propensity scores,and then derive the asymptotic properties of the proposed test statistic under both the null and local alternatives.Moreover,the authors conduct extensive simulations to verify the validity of the proposed test,and apply it to a human immunodeficiency virus data set to identify the important predictors for the conditional quantiles of the censored viral load.
基金supported by the National Natural Science Foundation of China(32070557,32470657,and 32270673).
文摘Current methods used in genome-wide association studies frequently lack power owing to their inability to detect heterogeneous associations and rare and multiallelic variants.To address these issues,quantile regression is integrated with a three(compressed)variance component multi-locus random-SNP-effect mixed linear model(3VmrMLM)to propose q3VmrMLM for detecting heterogeneous quantitative trait nucleotides(QTNs)and QTN-by-environment interactions(QEIs),and then design haplotype-based q3VmrMLM(q3VmrMLM-Hap)for identifying multiallelic haplotypes and rare variants.In Monte Carlo simulation studies,q3VmrMLM had higher power than 3VmrMLM,sequence kernel association test(SKAT),and integrated quantile rank test(iQRAT).In a re-analysis of 10 traits in 1439 rice hybrids,261 known genes were identified only by q3VmrMLM and q3VmrMLM-Hap,whereas 175 known genes were detected by both the new and existing methods.Of all the significant QTNs with known genes,q3VmrMLM(179:140 variance heterogeneity and 157 quantile effect heterogeneity)found more heterogeneous QTNs than 3VmrMLM(123),SKAT(27),and iQRAT(29);q3VmrMLM-Hap(121)mapped more lowfrequency(<0.05)QTNs than q3VmrMLM(51),3VmrMLM(43),SKAT(11),and iQRAT(12);and q3VmrMLM-Hap(12),q3VmrMLM(16),and 3VmrMLM(12)had similar power in identifying gene-by-environment interactions.All significant and suggested QTNs achieved the highest predictive accuracy(r=0.9045).In conclusion,this study describes a new and complementary approach to mining genes and unraveling the genetic architecture of complex traits in crops.
基金Supported by National Key R&D Program of China(Grant No.102022YFA1003701)National Natural Science Foundation of China(Grant No.12271472,12231017,12001479)Natural Science Foundation of Yunnan Province(Grant No.202101AU070073,202201AT070101)。
文摘Quantile regression is widely used in variable relationship research for statistical learning.Traditional quantile regression model is based on vector-valued covariates and can be efficiently estimated via traditional estimation methods.However,many modern applications involve tensor data with the intrinsic tensor structure.Traditional quantile regression can not deal with tensor regression issues well.To this end,we consider a tensor quantile regression with tensor-valued covariates and develop a novel variational Bayesian estimation approach to make estimation and prediction based on the asymmetric Laplace model and the CANDECOMP/PARAFAC decomposition of tensor coefficients.To incorporate the sparsity of tensor coefficients,we consider the multiway shrinkage priors for marginal factor vectors of tensor coefficients.The key idea of the proposed method is to efficiently combine the prior structural information of tensor and utilize the matricization of tensor decomposition to simplify the complexity of tensor coefficient estimation.The coordinate ascent algorithm is employed to optimize variational lower bound.Simulation studies and a real example show the numerical performances of the proposed method.
基金supported by the National Natural Science Foundation of China(12071348)the Key Scientific Research Foundation of Henan Educational Committee(24A110001)Key Laboratory of Intelligent Computing and Applications(Ministry of Education),Tongji University,China.
文摘In this paper,we focus on the problem of nonparametric quantile regression with left-truncated and right-censored data.Based on Nadaraya-Watson(NW)Kernel smoother and the technique of local linear(LL)smoother,we construct the NW and LL estimators of the conditional quantile.Under strong mixing assumptions,we establish asymptotic representation and asymptotic normality of the estimators.Finite sample behavior of the estimators is investigated via simulation,and a real data example is used to illustrate the application of the proposed methods.
基金funding support from the National Science Foundation CNS-1739823 is gratefully acknowledgedXin Zhang would like to thank the National Science Foundation(OISE-2330502,CBET-2047165,and CBET-2025826)and the Belmont Forum for support.
文摘Sudden reductions in crop yield(i.e.,yield shocks)severely disrupt the food supply,intensify food insecurity,depress farmers'welfare,and worsen a country's economic conditions.Here,we study the spatiotemporal patterns of wheat yield shocks,quantified by the lower quantiles of yield fluctuations,in 86 countries over 30 years.Furthermore,we assess the relationships between shocks and their key ecological and socioeconomic drivers using quantile regression based on statistical(linear quantile mixed model)and machine learning(quantile random forest)models.Using a panel dataset that captures spatiotemporal patterns of yield shocks and possible drivers in 86 countries,we find that the severity of yield shocks has been increasing globally since 1997.Moreover,our cross-validation exercise shows that quantile random forest outperforms the linear quantile regression model.Despite this performance difference,both models consistently reveal that the severity of shocks is associated with higher weather stress,nitrogen fertilizer application rate,and gross domestic product(GDP)per capita(a typical indicator for economic and technological advancement in a country).While the unexpected negative association between more severe wheat yield shocks and higher fertilizer application rate and GDP per capita does not imply a direct causal effect,they indicate that the advancement in wheat production has been primarily on achieving higher yields and less on lowering the possibility and magnitude of sharp yield reductions.Hence,in the context of growing extreme weather stress,there is a critical need to enhance the technology and management practices that mitigate yield shocks to improve the resilience of the world food systems.
基金The National Natural Science Foundation of China(42261042)The Hunan Provincial Natural Science Foundation(2024JJ7410)The Hunan Provincial Graduate Student Research and Innovation Program(CX20221095)。
文摘A comprehensive evaluation index system is constructed,and the entropy weight TOPSIS method isused to measure the optimization level of the digital economy and tourism industry structure of 30 provinces inChina from 2012 to 2021.Moreover,models such as quantile regression and panel threshold are used to explorethe influence of the digital economy(DIG)on the optimization of the tourism industry structure(TIS)as well as itstransmission mechanism.The study reveals that DIG significantly promotes TIS,which remains valid after endogeneity and robustness tests;the impact of DIG on TIS exhibited a“U-shape”effect that first decreases and thenincreases,and its highest significance is at the 90%quartile level.Threshold model tests revealed a nonlinearthreshold effect with DIG and tourism total factor productivity(TTFP)as a single threshold and tourism technological progress index(TECH)as a double threshold,and the second threshold has the largest effect of 0.163.Mechanism analysis found that the mediating impact of the DIG on the TIS was mediated by increasing the TTFP,and the TECH accounted for the highest proportion of 12.15%.Regional analysis revealed that the role of DIG onthe TIS is Central>East>West>Northeast,and the empowering effect is more significant in the high digital economylevel area and the high tourism industry structure optimization area.
