Promoting Similarity of Model Sparsity Structures in Integrative Analysis

• Activity Number: 232
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Yuan Huang*+ and Runze Li and Jian Huang and Shuangge Ma
• Companies: Penn State and Penn State and University of Iowa and Yale
• Keywords: Integrative analysis ; Marker identification ; Model sparsity structure

Abstract

Marker selection suffers from the inaccuracy and the lack of reproducibility due to the limitation of sample size. In this study, we conduct integrative analysis and marker selection under the heterogeneity model, which postulates that different datasets have possibly overlapping but not necessarily identical sets of markers. Under certain scenarios, it is reasonable to expect similarity of identified marker sets -- or equivalently, similarity of model sparsity structures -- across multiple datasets. However, the existing methods do not have a mechanism to explicitly promote such similarity. To solve this problem, we develop a novel sparse boosting method. This method uses a BIC/HDBIC criterion to select weak learners and encourage sparsity. A new penalty is introduced to promote similarity of model sparsity structures across datasets. The proposed method has an intuitive formulation and is generically applicable and computationally affordable. For demonstration, in numerical studies, we analyze right censored survival data under the AFT (accelerated failure time) model. Simulation shows that the proposed method outperforms alternatives with more accurate marker identification.