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Conditional Quantile Estimation with Truncated,Censored and Dependent Data

Conditional Quantile Estimation with Truncated,Censored and Dependent Data
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摘要 This paper deals with the conditional quantile estimation based on left-truncated and right-censored data.Assuming that the observations with multivariate covariates form a stationary α-mixing sequence,the authors derive the strong convergence with rate,strong representation as well as asymptotic normality of the conditional quantile estimator.Also,a Berry-Esseen-type bound for the estimator is established.In addition,the finite sample behavior of the estimator is investigated via simulations.
作者 Hanying LIANG Deli LI Tianxuan MIAO
机构地区 Department of Mathematics Department of Mathematical Sciences
出处 《Chinese Annals of Mathematics,Series B》 SCIE CSCD 2015年第6期969-990,共22页 数学年刊（B辑英文版）
基金 supported by the National Natural Science Foundation of China(No.11271286) the Specialized Research Fund for the Doctor Program of Higher Education of China(No.20120072110007) a grant from the Natural Sciences and Engineering Research Council of Canada
关键词 Berry-Esseen-type bound Conditional quantile estimator Strong rep-resentation Truncated and censored data Α-MIXING 截断和 估计 右删失数据 强收敛速度 渐近正态性 混合序列 有限样本 协变量
分类号 O212.1 [理学—概率论与数理统计]
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参考文献31

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Chinese Annals of Mathematics,Series B
2015年 第6期

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