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Activity Number: 17
Type: Topic Contributed
Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #315914 View Presentation
Title: Estimating a Low-Rank Covariance Matrix for Spatial Data
Author(s): Tapabrata Maiti* and Siddhartha Nandy and Chae Young Lim
Companies: Michigan State University and Michigan State University and Michigan State University
Keywords: Covariance Matrix ; Estimation ; Group LASSO ; Low Rank ; Non-stationary ; Spatial
Abstract:

We are interested in estimating a low rank covariance matrix for spatial data. We consider the spatial covariance matrix of the process is decomposed into two components: a diagonal matrix coming from measurement error process and a low rank covariance matrix which has a non-stationary structure. We propose a two-step approach using group LASSO type shrinkage estimation technique for estimating the rank of the covariance matrix and the matrix itself. A block coordinate descent method for a block multi-convex function under regularizing constraints is utilized to implement the proposed approach. We also provide theoretical and numerical validation of our method.


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