TY - CHAP
T1 - Model selection and information criterion
AU - Murata, Noboru
AU - Park, Hyeyoung
PY - 2009/12/1
Y1 - 2009/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84891459804&partnerID=8YFLogxK
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U2 - 10.1007/978-0-387-84816-7_14
DO - 10.1007/978-0-387-84816-7_14
M3 - Chapter
AN - SCOPUS:84891459804
SN - 9780387848150
SP - 333
EP - 354
BT - Information Theory and Statistical Learning
PB - Springer US
ER -