Non-Parametric Empirical Bayes Procedures

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...

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

Parametric Empirical Bayes Test and Its Application to Selection of Wavelet Threshold

In this article, we propose a new method for selecting level dependent threshold in wavelet shrinkage using the empirical Bayes framework. We employ both Bayesian and frequentist testing hypothesis instead of point estimation method. The best test yields the best prior and hence the more appropriate wavelet thresholds. The standard model functions are used to illustrate the performance of the p...