Jul 31, 2013

Ridge regression in prediction problems: automatic choice of the ridge parameter

Genetic Epidemiology
Erika Cule, Maria De Iorio

Abstract

To date, numerous genetic variants have been identified as associated with diverse phenotypic traits. However, identified associations generally explain only a small proportion of trait heritability and the predictive power of models incorporating only known-associated variants has been small. Multiple regression is a popular framework in which to consider the joint effect of many genetic variants simultaneously. Ordinary multiple regression is seldom appropriate in the context of genetic data, due to the high dimensionality of the data and the correlation structure among the predictors. There has been a resurgence of interest in the use of penalised regression techniques to circumvent these difficulties. In this paper, we focus on ridge regression, a penalised regression approach that has been shown to offer good performance in multivariate prediction problems. One challenge in the application of ridge regression is the choice of the ridge parameter that controls the amount of shrinkage of the regression coefficients. We present a method to determine the ridge parameter based on the data, with the aim of good performance in high-dimensional prediction problems. We establish a theoretical justification for our approach, and dem...Continue Reading

Mentioned in this Paper

Computer Programs and Programming
Receiver Operating Characteristic
Manic Disorder
Regression Analysis
Genetic Predisposition to Disease
Single Nucleotide Polymorphism
EAF2 gene
Variation (Genetics)

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