Speaker: Jin Wang, Northern Arizona University

Abstract: In an increasing variety of recent applications, data sets differ from traditional ones in having a very large number of explanatory variables. Variable selection is especially important and the traditional variable selection procedures fail to work in such cases. Here I will introduce a modern variable selection method, the elastic-net penalty method. Important properties of the method will be discussed and its application to a real research problem will also be given.