Conditional quantile estimation with auxiliary information for left-truncated and dependent data

被引：7

作者：

Liang, Han-Ying ^{[1
,2
,3
]}

de Una-Alvarez, Jacobo ^{[2
,3
]}

机构：

[1] Tongji Univ, Dept Math, Shanghai 200092, Peoples R China

[2] Univ Vigo, Dept Stat, Vigo 36310, Spain

[3] Univ Vigo, OR, Fac Ciencias Econ & Empresariales, Vigo 36310, Spain

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2011年 / 141卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Asymptotic normality; Conditional quantile estimator; Truncated data; alpha-Mixing; Auxiliary information; UNIFORM-CONVERGENCE RATE; RIGHT CENSORED-DATA; FAILURE TIME DATA; EMPIRICAL LIKELIHOOD; NONPARAMETRIC-ESTIMATION; ASYMPTOTIC PROPERTIES; KERNEL ESTIMATOR; SURVIVAL-DATA; REGRESSION; MODEL;

D O I：

10.1016/j.jspi.2011.05.012

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this paper, the empirical likelihood method is used to define a new estimator of conditional quantile in the presence of auxiliary information for the left-truncation model. The asymptotic normality of the estimator is established when the data exhibit some kind of dependence. It is assumed that the lifetime observations with multivariate covariates form a stationary a-mixing sequence. The result shows that the asymptotic variance of the proposed estimator is not larger than that of standard kernel estimator. Finite sample behavior of the estimator is investigated via simulations too. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：3475 / 3488

页数：14

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