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Simple measures of uncertainty for model selection

Author

Listed:
  • Xiaohui Liu

    (Jiangxi University of Finance and Economics)

  • Yuanyuan Li

    (University of California, Davis)

  • Jiming Jiang

    (University of California, Davis)

Abstract

We develop two simple measures of uncertainty for a model selection procedure. The first measure is similar in spirit to confidence set in parameter estimation; the second measure is focusing on error in model selection. The proposed methods are simpler, both conceptually and computationally, than the existing measures of uncertainty in model selection. We recognize major differences between model selection and traditional estimation or prediction problems, and propose reasonable frameworks, under which these measures are developed, and their theoretical properties are established. Empirical studies demonstrate performance of the proposed measures, their superiority over the existing methods, and their relevance to real-life applications.

Suggested Citation

  • Xiaohui Liu & Yuanyuan Li & Jiming Jiang, 2021. "Simple measures of uncertainty for model selection," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 673-692, September.
    • Handle: RePEc:spr:testjl:v:30:y:2021:i:3:d:10.1007_s11749-020-00737-9
    DOI: 10.1007/s11749-020-00737-9
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    References listed on IDEAS

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    Cited by:

    1. Faguang Wen & Jiming Jiang & Yihui Luan, 2024. "Model Selection Path and Construction of Model Confidence Set under High-Dimensional Variables," Mathematics, MDPI, vol. 12(5), pages 1-21, February.

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