具有协变量测量误差和不可忽略的缺失响应的线性量回归的统计推断

IF 0.9 4区 数学 Q3 STATISTICS & PROBABILITY
Metrika Pub Date : 2024-05-18 DOI:10.1007/s00184-024-00967-z
Xiaowen Liang, Boping Tian
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引用次数: 0

摘要

本文考虑对具有协变量测量误差和不可忽略的缺失响应的线性模型进行量化回归估计。首先,通过偏差修正的量化损失函数消除测量误差的影响。为了处理不可忽略的缺失中的可识别性问题,使用了非响应工具。然后,基于反概率加权方法,我们提出了一种加权偏差校正量子损失函数,它既能处理不可忽略的缺失,又能处理协变量测量误差。在一定的正则条件下,我们建立了所提估计器的渐近特性。蒙特卡罗模拟和经验数据分析说明了所提方法的有限样本性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Statistical inference for linear quantile regression with measurement error in covariates and nonignorable missing responses

Statistical inference for linear quantile regression with measurement error in covariates and nonignorable missing responses

In this paper, we consider quantile regression estimation for linear models with covariate measurement errors and nonignorable missing responses. Firstly, the influence of measurement errors is eliminated through the bias-corrected quantile loss function. To handle the identifiability issue in the nonignorable missing, a nonresponse instrument is used. Then, based on the inverse probability weighting approach, we propose a weighted bias-corrected quantile loss function that can handle both nonignorable missingness and covariate measurement errors. Under certain regularity conditions, we establish the asymptotic properties of the proposed estimators. The finite sample performance of the proposed method is illustrated by Monte Carlo simulations and an empirical data analysis.

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来源期刊
Metrika
Metrika 数学-统计学与概率论
CiteScore
1.50
自引率
14.30%
发文量
39
审稿时长
6-12 weeks
期刊介绍: Metrika is an international journal for theoretical and applied statistics. Metrika publishes original research papers in the field of mathematical statistics and statistical methods. Great importance is attached to new developments in theoretical statistics, statistical modeling and to actual innovative applicability of the proposed statistical methods and results. Topics of interest include, without being limited to, multivariate analysis, high dimensional statistics and nonparametric statistics; categorical data analysis and latent variable models; reliability, lifetime data analysis and statistics in engineering sciences.
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