I have got a model with one continuous dependent variable and 100 categorical predictors (candidate SNP´s) with 3 levels each (homozygous for one allele, heterozygous, homozygous for the other allele) and 288 observations. What is the best method to select a more parsimonious model ? (with, say, just 5-20 independent variables?).
I discriminate many small samples. So, it is difficult problem to evaluate the best model. I developed "k-fold cross validation". And I choose K=100. And I choose the best model having the minimum mean of error rates in the validation samples. See "Comparison of Linear Discriminant Function by K-fold Cross Validation. Data Analytics 2014". In the regression analysis, you choose the minimum summation of deviation square as the best model in the validation samples.
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