Author

• Abbas Khalili

Abstract

Variable (feature) selection has attracted much attention in
contemporary statistical learning and recent scientific research. This is
mainly due to the rapid advancement in modern technology that allows
scientists to collect data of unprecedented size and complexity. One type
of statistical problem in such applications is concerned with modeling
an output variable as a function of a small subset of a large number of
features. In certain applications, the data samples may even be coming
from multiple subpopulations. In these cases, selecting the correct
predictive features (variables) for each subpopulation is crucial. The
classical best subset selection methods are computationally too expensive
for many modern statistical applications. New variable selection
methods have been successfully developed over the last decade to deal
with large numbers of variables. They have been designed for simultaneously
selecting important variables and estimating their effects in a
statistical model. In this article, we present an overview of the recent
developments in theory, methods, and implementations for the variable
selection problem in finite mixture of regression models.

Keywords

• em algorithm
• mixture models
• mixture of regression models
• penalized likelihood
• Regularization
• variable selection