2001
DOI: 10.1002/gepi.2001.21.s1.s272
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Marker Selection by Akaike Information Criterion and Bayesian Information Criterion

Abstract: 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 selecti… Show more

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Cited by 70 publications
(52 citation statements)
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References 8 publications
(9 reference statements)
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“…In Results, we report unadjusted P values. After consideration of SNP genotypes individually, all SNPs remaining significant after adjustment by permutation were included in a stepwise selection model using Akaike's Information Criterion to select the most parsimonious model (27). SNPs that were significant (nominal P < 0.05) after adjustment for each other were included in the final model (28).…”
Section: Methodsmentioning
confidence: 99%
“…In Results, we report unadjusted P values. After consideration of SNP genotypes individually, all SNPs remaining significant after adjustment by permutation were included in a stepwise selection model using Akaike's Information Criterion to select the most parsimonious model (27). SNPs that were significant (nominal P < 0.05) after adjustment for each other were included in the final model (28).…”
Section: Methodsmentioning
confidence: 99%
“…After consideration of SNP genotypes individually, all significant SNPs were included in a stepwise selection model using Akaike's Information Criterion to select the most parsimonious model (15). SNPs that remained significant (P V 0.05) after adjustment for each other were included in the final model (16).…”
Section: Methodsmentioning
confidence: 99%
“…27 Multivariable regression analysis of the TVR risk was performed with all IL-10 polymorphisms, using a stepwise backward selection algorithm. In the final step, clinical variables associated with TVR, also including age and gender, were entered into the regression model.…”
Section: Methodsmentioning
confidence: 99%