High dimensional data are nowadays encountered in various branches of science. Variable selection techniques play a key role in analyzing high dimensional data. Generally two approaches for variable selection in the high dimensional data setting are considered — forward selection methods and penalization methods. In the former, variables are introduced in the model one at a time depending on th...

A challenging aspect of applying stochastic programming in a dynamic setting is to construct a set of discrete scenarios that well represents multivariate stochastic processes for uncertain parameters. Often this is done by generating a scenario tree using a statistical procedure and then reducing its size while maintaining its statistical properties. In this paper, we test a new scenario reduc...