Bayesian elastic net based on empirical likelihood

The Multiple Bayesian Elastic Net

We propose the multiple Bayesian elastic net (abbreviated as MBEN), a new regularization and variable selection method. High dimensional and highly correlated data are commonplace. In such situations, maximum likelihood procedures typically fail—their estimates are unstable, and have large variance. To address this problem, a number of shrinkage methods have been proposed, including ridge regre...

Bayesian computation via empirical likelihood.

Approximate Bayesian computation has become an essential tool for the analysis of complex stochastic models when the likelihood function is numerically unavailable. However, the well-established statistical method of empirical likelihood provides another route to such settings that bypasses simulations from the model and the choices of the approximate Bayesian computation parameters (summary st...

Bayesian Quantile Regression with Adaptive Elastic Net Penalty for Longitudinal Data

Longitudinal studies include the important parts of epidemiological surveys, clinical trials and social studies. In longitudinal studies, measurement of the responses is conducted repeatedly through time. Often, the main goal is to characterize the change in responses over time and the factors that influence the change. Recently, to analyze this kind of data, quantile regression has been taken ...

Elastic Net-based Parcellation (ENPA)

Empirical Likelihood Approach and its Application on Survival Analysis

A number of nonparametric methods exist when studying the population and its parameters in the situation when the distribution is unknown. Some of them such as "resampling bootstrap method" are based on resampling from an initial sample. In this article empirical likelihood approach is introduced as a nonparametric method for more efficient use of auxiliary information to construct...