Statistics Papers

Document Type

Technical Report

Date of this Version

5-2016

Publication Source

Journal of the American Statistical Association

Volume

111

Issue

513

Start Page

253

Last Page

265

DOI

10.1080/01621459.2014.1001067

Abstract

Covariance structure plays an important role in high-dimensional statistical inference. In a range of applications including imaging analysis and fMRI studies, random variables are observed on a lattice graph. In such a setting, it is important to account for the lattice structure when estimating the covariance operator. In this article, we consider both minimax and adaptive estimation of the covariance operator over collections of polynomially decaying and exponentially decaying parameter spaces. We first establish the minimax rates of convergence for estimating the covariance operator under the operator norm. The results show that the dimension of the lattice graph significantly affects the optimal rates convergence, often much more so than the dimension of the random variables. We then consider adaptive estimation of the covariance operator. A fully data-driven block thresholding procedure is proposed and is shown to be adaptively rate optimal simultaneously over a wide range of polynomially decaying and exponentially decaying parameter spaces. The adaptive block thresholding procedure is easy to implement, and numerical experiments are carried out to illustrate the merit of the procedure. Supplementary materials for this article are available online.

Copyright/Permission Statement

This is an Accepted Manuscript of an article published by Taylor & Francis in the Journal of the American Statistical Association on 05 May 2016, available online: http://dx.doi.org/10.1080/01621459.2014.1001067

Keywords

adaptive estimation, block thresholding, covariance matrix, covariance operator, lattice graph, minimax estimation, operator norm, optimal rate of convergence

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Date Posted: 25 October 2018

This document has been peer reviewed.