Abstract

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.

摘要

在横断面调查和流行病学队列研究中通常会观察到长度偏向的抽样数据。由于研究设计或意外，协变量向量的某些分量经常丢失。本文考虑了具有长度偏差采样数据和缺失协变量的变换模型的统计推断。当知道选择概率，非参数估计或参数估计时，提出了未知回归参数的重新估算程序。研究了所得估计量的大样本特性。仿真研究展示了所提出方法的实用性和效率。