Variable Selection for Adaptive MAVE

• Activity Number: 553
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Computing
• Author(s): Hossein Moradi Rekabdarkolaee* and Qin Wang and Edward Boone
• Companies: Virginia Commonwealth University and Virginia Commonwealth University and Virginia Commonwealth University
• Keywords: Sufficient dimension reduction ; adaptive MAVE ; Shrinkage estimation ; variable selection

Abstract

Dimension reduction and variable selection play important roles in high dimensional data analysis. MAVE (minimum average variance estimation) is an efficient approach to estimate the central mean space. However, it is not robust to outliers and/or heavy tailed distribution and cannot select variable automatically. Therefore, the estimated reduction space might not be true reduced space. Adaptive MAVE was introduced in order to adapt to different error distributions but this method cannot select variables automatically, too. In this paper, we used variable selection on adaptive MAVE by shrinkage estimation and its combination with Lasso to produce an effective dimension reduction that enhances its practical applicability. The efficacy of the new approach is illustrated through a simulation study and we used our method to analyze a real data.