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Finite Population Survey Sampling: An Unapologetic Bayesian Perspective

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Abstract

This article attempts to offer some perspectives on Bayesian inference for finite population quantities when the units in the population are assumed to exhibit complex dependencies. Beginning with an overview of Bayesian hierarchical models, including some that yield design-based Horvitz-Thompson estimators, the article proceeds to introduce dependence in finite populations and sets out inferential frameworks for ignorable and nonignorable responses. Multivariate dependencies using graphical models and spatial processes are discussed and some salient features of two recent analyses for spatial finite populations are presented.

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Acknowledgements

The author wishes to thank the editors and an anonymous referee for valuable feedback. The author is especially grateful to Professors Roderick J. Little and Trivellore Raghunathan from the University of Michigan, Ann Arbor, U.S.A., and Professor J.N.K. Rao from Carleton University, Ottawa, Canada, for insightful discussions on inference for finite populations. The work of the author has been supported, in part, by the National Science Foundation (NSF) from grants NSF/DMS 1916349 and NSF/DMS 2113778, by the National Institute of Environmental Health Sciences (NIEHS) from grants R01ES030210 and R01ES027027 and by the National Institute of General Medical Science from grant R01GM148761.

Funding

The work of the author has been supported, in part, by the National Science Foundation (NSF) from grants NSF/DMS 1916349 and NSF/DMS 2113778, by the National Institute of Environmental Health Sciences (NIEHS) from grants R01ES030210 and R01ES027027 and by the National Institute of General Medical Science from grant R01GM148761.

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  1. UCLA Department of Biostatistics; UCLA Department of Statistics and Data Science, University of California Los Angeles, 650 Charles E. Young Drive South, Los Angeles, 90095-1772, California, USA

    Sudipto Banerjee

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Banerjee, S. Finite Population Survey Sampling: An Unapologetic Bayesian Perspective. Sankhya A (2024). https://doi.org/10.1007/s13171-024-00348-8

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