Integrative genetic risk prediction using non-parametric empirical Bayes classification

Integrative genetic risk prediction using non-parametric empirical Bayes classification.

Genetic risk prediction is an important component of individualized medicine, but prediction accuracies remain low for many complex diseases. A fundamental limitation is the sample sizes of the studies on which the prediction algorithms are trained. One way to increase the effective sample size is to integrate information from previously existing studies. However, it can be difficult to find ex...

Application of Non Parametric Empirical Bayes Estimation to High Dimensional Classification

We consider the problem of classification using high dimensional features’ space. In a paper by Bickel and Levina (2004), it is recommended to use naive-Bayes classifiers, that is, to treat the features as if they are statistically independent. Consider now a sparse setup, where only a few of the features are informative for classification. Fan and Fan (2008), suggested a variable selection and...

Two semi parametric empirical Bayes estimators

Parametric empirical Bayes PEB may perform poorly when the assumed prior distribution is seriously invalid Nonparametric empirical Bayes NEB is more robust since it imposes no restric tion on the prior But compared with the PEB the NEB may be ine cient for small to medium samples due to the large variation and under dispersion of the NPMLE of the prior Using Monte Carlo simulations we compare t...

Parametric Empirical Bayes Methods for Microarray Data

Parametric Empirical Bayes Methods for Microarrays