Pseudo empirical likelihood inference for nonprobability survey samples

被引:3
|
作者
Chen, Yilin [1 ]
Li, Pengfei [2 ]
Rao, J. N. K. [3 ]
Wu, Changbao [2 ]
机构
[1] Hosp Sick Children, Child Hlth Evaluat Sci, Toronto, ON, Canada
[2] Univ Waterloo, Dept Stat & Actuarial Sci, Waterloo, ON, Canada
[3] Carleton Univ, Sch Math & Stat, Ottawa, ON, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Auxiliary information; confidence interval; design-based inference; doubly robust estimator; empirical likelihood ratio statistic; joint randomization; propensity score; reference probability sample; RATIO CONFIDENCE-INTERVALS; FINITE POPULATIONS; INFORMATION; VARIANCE; ESTIMATORS;
D O I
10.1002/cjs.11708
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 article, we first provide an overview of two major developments on complex survey data analysis: the empirical likelihood methods and statistical inference with nonprobability survey samples. We highlight the important research contributions to the field of survey sampling in general and the two topics in particular by Canadian survey statisticians. We then propose new inferential procedures for analyzing nonprobability survey samples through the pseudo empirical likelihood approach. The proposed methods lead to point estimators asymptotically equivalent to those discussed in the recent literature but with more desirable features on confidence intervals such as range-respecting and data-driven orientation. Results from a simulation study demonstrate the superiority of the proposed methods in dealing with binary response variables.
引用
收藏
页码:1166 / 1185
页数:20
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