Nonidentical twins: Comparison of frequentist and Bayesian lasso for XXXX models.

@entity5 @entity1 @entity797 @entity1072
UNASSIGNED: One important task in translational @entity5 research is the search for new prognostic biomarkers to improve survival prognosis for @entity1 . The use of high-throughput technologies allows simultaneous measurement of genome-wide gene expression or other genomic data for all @entity1 in a clinical trial. Penalized likelihood methods such as lasso regression can be applied to such high-dimensional data, where the number of (genomic) covariables is usually much larger than the sample size. There is a connection between the lasso and the Bayesian regression model with independent Laplace priors on the regression parameters, and understanding this connection has been useful for understanding the properties of lasso estimates in linear models (e.g. Park and Casella, ). In this paper, we study the lasso in the frequentist and Bayesian frameworks in the context of @entity797 models. For the Bayesian lasso we extend the approach by Lee et  al. (). In particular, we impose the lasso penalty only on the genome features, but not on relevant clinical covariates, to allow the mandatory inclusion of important established factors. We investigate the models in high- and low-dimensional simulation settings and in an application to @entity1072 .
