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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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    1. Yuan, Zheng & Yang, Yuhong, 2005. "Combining Linear Regression Models: When and How?," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1202-1214, December.
    2. Chao Zheng & Davide Ferrari & Michael Zhang & Paul Baird, 2019. "Ranking the importance of genetic factors by variable‐selection confidence sets," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(3), pages 727-749, April.
    3. Datta, Gauri S. & Hall, Peter & Mandal, Abhyuday, 2011. "Model Selection by Testing for the Presence of Small-Area Effects, and Application to Area-Level Data," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 362-374.
    4. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258, October.
    5. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    6. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    7. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    8. Xie, Minge & Singh, Kesar & Zhang, Cun-Hui, 2009. "Confidence Intervals for Population Ranks in the Presence of Ties and Near Ties," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 775-788.
    9. Xiaotong Shen & Wei Pan & Yunzhang Zhu, 2012. "Likelihood-Based Selection and Sharp Parameter Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 223-232, March.
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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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