Model Selection and Dimension Reduction

In many fields, recent developments have led to an explosion in the number of measurements that can be collected in various settings. While at first exciting, having so many predictors can lead to serious high-dimensional problems. With so many predictors, how does a researcher identify the most important ones? If there are too many predictors (p > n), how can the analysis be carried out? These high-dimension problems that arise from modern data sets have called for a major expansion of the classical statistical toolbox for analyzing data. This workshop will cover the biggest issues in performing high-dimension analysis and will provide the tools needed to be successful; techniques including factor analy- sis to reduce the dimensionality of the predictors (dimension reduction) and LASSO to select the best predictors from those available (model selection), will be covered. These techniques will be presented with corresponding examples and accompanying R scripts that can be done along with the presentation.