Model selection when a key parameter is constrained to be in an interval

M. Z. Hossain, M. L. King

Research output: Contribution to journalArticlepeer-review


This article considers model selection procedures based on choosing the model with the largest maximized log-likelihood minus a penalty, when key parameters are restricted to be in a closed interval. Its main emphasis is how these penalties might be chosen in small samples to give good properties of the resultant procedure. We illustrate two model selection problems in the context of Box-Cox transformations and their application to the linear regression model. Simulation results for both problems indicate that the new procedure clearly dominates existing procedures in terms of having higher probabilities of correctly selecting the true model.

Original languageEnglish
Pages (from-to)1270-1280
Number of pages11
JournalCommunications in Statistics: Simulation and Computation
Issue number7
Publication statusPublished - Aug 2008
Externally publishedYes


  • Box-Cox transformations
  • Controlled information criterion
  • Nuisance parameters
  • Parametric bootstrap
  • Regression model

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation


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