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Marker Selection by Akaike Information Criterion and Bayesian Information Criterion
Author(s) -
Li Wentian,
Nyholt Dale R.
Publication year - 2001
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.2001.21.s1.s272
Subject(s) - bayesian information criterion , akaike information criterion , statistics , mathematics , information criteria , deviance information criterion , stepwise regression , bayesian probability , linear discriminant analysis , logistic regression , model selection , selection (genetic algorithm) , bayesian inference , artificial intelligence , computer science
We carried out a discriminant analysis with identity by descent (IBD) at each marker as inputs, and the sib pair type (affected‐affected versus affected‐unaffected) as the output. Using simple logistic regression for this discriminant analysis, we illustrate the importance of comparing models with different number of parameters. Such model comparisons are best carried out using either the Akaike information criterion (AIC) or the Bayesian information criterion (BIC). When AIC (or BIC) stepwise variable selection was applied to the German Asthma data set, a group of markers were selected which provide the best fit to the data (assuming an additive effect). Interestingly, these 25–26 markers were not identical to those with the highest (in magnitude) single‐locus lod scores. © 2001 Wiley‐Liss, Inc.