Electronic Journal of Statistics

Bayesian pairwise estimation under dependent informative sampling

Matthew R. Williams and Terrance D. Savitsky

Full-text: Open access

Abstract

An informative sampling design leads to the selection of units whose inclusion probabilities are correlated with the response variable of interest. Inference under the population model performed on the resulting observed sample, without adjustment, will be biased for the population generative model. One approach that produces asymptotically unbiased inference employs marginal inclusion probabilities to form sampling weights used to exponentiate each likelihood contribution of a pseudo likelihood used to form a pseudo posterior distribution. Conditions for posterior consistency restrict applicable sampling designs to those under which pairwise inclusion dependencies asymptotically limit to $0$. There are many sampling designs excluded by this restriction; for example, a multi-stage design that samples individuals within households. Viewing each household as a population, the dependence among individuals does not attenuate. We propose a more targeted approach in this paper for inference focused on pairs of individuals or sampled units; for example, the substance use of one spouse in a shared household, conditioned on the substance use of the other spouse. We formulate the pseudo likelihood with weights based on pairwise or second order probabilities and demonstrate consistency, removing the requirement for asymptotic independence and replacing it with restrictions on higher order selection probabilities. Our approach provides a nearly automated estimation procedure applicable to any model specified by the data analyst. We demonstrate our method on the National Survey on Drug Use and Health.

Article information

Source
Electron. J. Statist., Volume 12, Number 1 (2018), 1631-1661.

Dates
Received: September 2017
First available in Project Euclid: 26 May 2018

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1527300143

Digital Object Identifier
doi:10.1214/18-EJS1435

Mathematical Reviews number (MathSciNet)
MR3806435

Zentralblatt MATH identifier
06875411

Keywords
Survey sampling sampling weights quantile regression non-linear regression Markov chain Monte Carlo

Rights
Creative Commons Attribution 4.0 International License.

Citation

Williams, Matthew R.; Savitsky, Terrance D. Bayesian pairwise estimation under dependent informative sampling. Electron. J. Statist. 12 (2018), no. 1, 1631--1661. doi:10.1214/18-EJS1435. https://projecteuclid.org/euclid.ejs/1527300143


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