Sample design for analysis using high‐influence probability sampling

Robert G. Clark and David G. Steel

Journal of the Royal Statistical Society Series A, 2022, vol. 185, issue 4, 1733-1756

Abstract: Sample designs are typically developed to estimate summary statistics such as means, proportions and prevalences. Analytical outputs may also be a priority but there are fewer methods and results on how to efficiently design samples for the fitting and estimation of statistical models. This paper develops a general approach for determining efficient sampling designs for probability‐weighted maximum likelihood estimators and considers application to generalized linear models. We allow for non‐ignorable sampling, including outcome‐dependent sampling. The new designs have probabilities of selection closely related to influence statistics such as dfbeta and Cook's distance. The new approach is shown to perform well in a simulation based on data from the New Zealand Health Survey.

Date: 2022
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https://doi.org/10.1111/rssa.12916

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Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssa:v:185:y:2022:i:4:p:1733-1756

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