Model selection and information criterion

Noboru Murata*, Hyeyoung Park

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)


In this chapter, a problem of estimating model parameters from observed data is considered such as regression and function approximation, and a method of evaluating the goodness of model is introduced. Starting from so-called leave-one-out cross-validation, and investigating asymptotic statistical properties of estimated parameters, a generalized Akaike's information criterion (AIC) is derived for selecting an appropriate model from several candidates. In addition to model selection, the concept of information criteria provides an assessment of the goodness of model in various situations. Finally, an optimization method using regularization is presented as an example.

Original languageEnglish
Title of host publicationInformation Theory and Statistical Learning
PublisherSpringer US
Number of pages22
ISBN (Print)9780387848150
Publication statusPublished - 2009 Dec 1

ASJC Scopus subject areas

  • Computer Science(all)

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