Reweighting estimators for the transformation models with length-biased sampling data and missing covariates

被引：0

作者：

Qiu, Zhiping ^{[1
,2
]}

Ma, Huijuan ^{[3
,4
]}

Shi, Jianhua ^{[5
]}

机构：

[1] Huaqiao Univ, Sch Stat, Xiamen, Peoples R China

[2] Huaqiao Univ, Res Ctr Appl Stat & Big Data, Xiamen, Peoples R China

[3] East China Normal Univ, Minist Educ, Key Lab Adv Theory & Applicat Stat & Data Sci, Shanghai, Peoples R China

[4] East China Normal Univ, Acad Stat & Interdisciplinary Sci, Shanghai, Peoples R China

[5] Minnan Normal Univ, Sch Math & Stat, Zhangzhou, Peoples R China

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2022年 / 51卷 / 13期

基金：

中国国家自然科学基金;

关键词：

Length-biased sampling; missing covariate data; transformation models; weighted estimating equation; PROPORTIONAL HAZARDS REGRESSION; PSEUDO-PARTIAL LIKELIHOOD; ADDITIVE RISK MODEL; FAILURE TIME MODEL; ESTIMATING EQUATION; SEMIPARAMETRIC REGRESSION; CASE-COHORT;

D O I：

10.1080/03610926.2020.1812653

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：

Length-biased sampling data are commonly observed in cross-sectional surveys and epidemiological cohort studies. Due to study design or accident, some components of the covariate vector are often missing. This article considers the statistical inference for the transformation models with length-biased sampling data and missing covariates. The reweighting estimating procedures are proposed for the unknown regression parameters when the selection probability is known, estimated non parametrically, or estimated parametrically. The large sample properties of the resulting estimators are studied. Simulation studies are presented to demonstrate the utility and efficiency of the proposed methods.

引用

页码：4252 / 4275

页数：24

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