Bootstrap inference for the finite population total under complex sampling designs
Abstract
Bootstrap is a useful tool for making statistical inference, but it may provide erroneous results under complex survey sampling. Most studies about bootstrap-based inference are developed under simple random sampling and stratified random sampling. In this paper, we propose a unified bootstrap method applicable to some complex sampling designs, including Poisson sampling and probability-proportional-to-size sampling. Two main features of the proposed bootstrap method are that studentization is used to make inference, and the finite population is bootstrapped based on a multinomial distribution by incorporating the sampling information. We show that the proposed bootstrap method is second-order accurate using the Edgeworth expansion. Two simulation studies are conducted to compare the proposed bootstrap method with the Wald-type method, which is widely used in survey sampling. Results show that the proposed bootstrap method is better in terms of coverage rate especially when sample size is limited.
Cite
@article{arxiv.1901.01645,
title = {Bootstrap inference for the finite population total under complex sampling designs},
author = {Zhonglei Wang and Jae Kwang Kim and Liuhua Peng},
journal= {arXiv preprint arXiv:1901.01645},
year = {2019}
}