Morris Hansen Lecture 2023: Model Selection and Its Important Roles in Surveys

Abstract We discuss some fundamental issues regarding the roles that statistical models play in surveys. Many survey applications “borrow strength” from statistical models such as regression models and mixed effects models, even though these popular statistical models rarely hold exactly, if at all, in the real world. Yet, the idea of borrowing strength carries on, and the practice proves to be useful in many cases. We discuss basic ideas of borrowing strength via a statistical model and suggest practical criteria for evaluating a model, knowing that it is likely to be incorrect in a strict sense. Furthermore, we discuss a model selection strategy, known as the fence methods, which can incorporate practical interests into the selection criterion. Examples are used for illustration. Further remarks on challenging problems are offered.

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Publication Details

Journal
Journal of Survey Statistics and Methodology
Published
2024-08-10
DOI
https://doi.org/10.1093/jssam/smae034
Citations
1
Primary Topic
Statistical Methods and Bayesian Inference
Type
article
Field-Weighted Citation Impact
0.71

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article

Morris Hansen Lecture 2023: Model Selection and Its Important Roles in Surveys

Jiming Jiang
1 citations
Journal of Survey Statistics and Methodology
Statistical Methods and Bayesian Inference
0.71
article

Morris Hansen Lecture 2023: Model Selection and Its Important Roles in Surveys

Jiming Jiang
article en
1 citations

Abstract

Abstract We discuss some fundamental issues regarding the roles that statistical models play in surveys. Many survey applications “borrow strength” from statistical models such as regression models and mixed effects models, even though these popular statistical models rarely hold exactly, if at all, in the real world. Yet, the idea of borrowing strength carries on, and the practice proves to be useful in many cases. We discuss basic ideas of borrowing strength via a statistical model and suggest practical criteria for evaluating a model, knowing that it is likely to be incorrect in a strict sense. Furthermore, we discuss a model selection strategy, known as the fence methods, which can incorporate practical interests into the selection criterion. Examples are used for illustration. Further remarks on challenging problems are offered.

Journal of Survey Statistics and MethodologyVol. 12(4)
University of California, Davis (US)
National Science Foundation
Openalex Percentile: Top 27%
Statistical Methods and Bayesian Inference
0.71
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Morris Hansen Lecture 2023: Model Selection and Its Important Roles in Surveys — Jiming Jiang · Journal of Survey Statistics and Methodology (2024) | TGRS Research Map | TGRS