Although genetic association studies have been with us for many years, even for the
simplest analyses there is little consensus on the most appropriate statistical procedures …

Bayesian statistics allow scientists to easily incorporate prior knowledge into their data
analysis. Nonetheless, the sheer amount of computational power that is required for …

The purpose of model selection algorithms such as All Subsets, Forward Selection and
Backward Elimination is to choose a linear model on the basis of the same set of data to …

Variable selection in the linear regression model takes many apparent faces from both
frequentist and Bayesian standpoints. In this paper we introduce a variable selection method …

Alle im vorigen Kapitel beschriebenen Problemstellungen besitzen eine wesentliche
Gemeinsamkeit: Eigenschaften einer Zielvariablen y sollen in Abhängigkeit von Kovariablen …

The growth of biostatistics has been phenomenal in recent years and has been marked by
considerable technical innovation in both methodology and computational practicality. One …

Logistic regression analysis of high-dimensional data, such as natural language text, poses
computational and statistical challenges. Maximum likelihood estimation often fails in these …

We describe studies in molecular profiling and biological pathway analysis that use sparse
latent factor and regression models for microarray gene expression data. We discuss breast …

Large-scale hypothesis testing problems, with hundreds or thousands of test statistics zi to
consider at once, have become familiar in current practice. Applications of popular analysis …

We consider the problem of variable selection in regression modeling in high-dimensional
spaces where there is known structure among the covariates. This is an unconventional …