Simple measures of uncertainty for model selection

• Xiaohui Liu ^[1] ; Yuanyuan Li ^[2] ; Jiming Jiang ^[2]
  1. [1] Jiangxi University of Finance and Economics

     Jiangxi University of Finance and Economics

     China

     
  2. [2] University of California System

     University of California System

     Estados Unidos

     
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 30, Nº. 3, 2021, págs. 673-692
• Idioma: inglés
• DOI: 10.1007/s11749-020-00737-9
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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.