Campus Units

Statistics

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

1-7-2019

Journal or Book Title

arxiv

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.

Comments

This pre-print is made available through arxiv: https://arxiv.org/abs/1901.01645.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

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