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Activity Number: 608
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #320169
Title: Flexible Modeling of Local Dependence in Variables with a Natural Ordering
Author(s): Guo Yu* and Jacob Bien
Companies: Cornell University and Cornell University
Keywords: local dependence ; ordered variable ; precision matrix ; variable bandwidth ; structured sparsity ; hierarchical group lasso

We propose a framework for flexibly modeling local dependence among variables having a known ordering. Our target of inference is the inverse of the Cholesky factor of the covariance matrix, which provides a simple interpretation for local dependence when only elements close to its diagonal are nonzero. The estimator is the solution to a convex, penalized Gaussian likelihood problem with a hierarchical group lasso penalty. The problem can be decomposed into independent subproblems which can be solved efficiently. Because of the convexity and decomposability of our formulation, the estimator has theoretical results that are not found in existing estimators attaining this structure. In particular, signed support recovery and estimation consistency in multiple norms are established. Empirical results show our estimator performing favorably compared to existing estimators. We apply our estimator to data from the HapMap project to flexibly model linkage disequilibrium.

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

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