Comparisons of Assumptions and Performance  

Oleh:

Kuha, Jouni

Jenis: Article from Bulletin/Magazine
Dalam koleksi: Sociological Methods and Research vol. 33 no. 02 (Nov. 2004), page 188.
Topik: Bayesian inference; Kullback-Leibler divergence; Mobility tables; Model selection; parsimoni; prediction

Isi artikel

The two most commonly used penalized model selection criteria, the Bayesian informatiion criterion (BIC) and akaike's informtation criteriior (AIC), are examined and compared. Their motivations as approximations of two different target quantities are discussed, and their performance in estimating those quantities is assessed. despite their different foundations, some similarities between the two statistic can be observed. for example, in analogus interpretations of their penalty terms. the behaviour of the criteria in selecting good models for observed date is examined with simulated data and also illustrated with the analysis of two well-known data sets on social mobility. it is argued that useful information for model selection can be obtained from using AIC and BIC together, particularly from trying as far as possible to find models favored by both criteria.

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