Online Program Home
My Program

Abstract Details

Activity Number: 314
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #320069
Title: CoCoLasso for High-Dimensional Error-in-Variables Regression
Author(s): Abhirup Datta* and Hui Zou
Companies: University of Minnesota and University of Minnesota
Keywords: Error-in-variables ; High-dimensional regression ; Lasso

We often face corrupted high dimensional data in many applications where missing data and measurement errors cannot be ignored. We propose a new method named CoCoLasso that is convex and can handle a general class of corrupted datasets including the cases of additive measurement error and random missing data. CoCoLasso can be reformulated as a modified Lasso problem and automatically enjoys the benefits of convexity for high-dimensional regression. Theoretically, we establish that the error bounds of CoCoLasso are comparable to those of the Lasso. We also derive finite sample and asymptotic sign-consistent selection property without requiring any specifi cation of the type of measurement error. We elucidate how standard cross validation techniques may be inefficient in presence of measurement error and develop a novel cross-validation technique for choosing tuning parameter for CoCoLasso. We demonstrate the superior performance of our method over the non-convex approach by simulation studies.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association