Digital Commons Network^™

Cost-Efficient Variable Selection Using Branching Lars, Li Hua Yue

Electronic Thesis and Dissertation Repository

Variable selection is a difficult problem in statistical model building. Identification of cost efficient diagnostic factors is very important to health researchers, but most variable selection methods do not take into account the cost of collecting data for the predictors. The trade off between statistical significance and cost of collecting data for the statistical model is our focus. A Branching LARS (BLARS) procedure has been developed that can select and estimate the important predictors to build a model not only good at prediction but also cost efficient. BLARS method is an extension of the LARS variable selection method to incorporate …

Model Selection With Information Criteria, Changjiang Xu

Electronic Thesis and Dissertation Repository

This thesis is on model selection using information criteria. The information criteria include generalized information criterion and a family of Bayesian information criteria. The properties and improvement of the information criteria are investigated.

We analyze nonasymptotic and asymptotic properties of the information criteria for linear models, probabilistic models, and high dimensional models, respectively. We give probability of selecting a model and compute the probability by Monte Carlo methods. We derive the conditions under which the criteria are consistent, underfitting, or overfitting.

We further propose new model selection procedures to improve the information criteria. The procedures combine the information criteria with …