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Template Matching and Change Point Detection by M-estimation by Arias-Castro, Ery; Zheng, Lin is a scholarly article available to read on EtoBox.

What is Template Matching and Change Point Detection by M-estimation about?

We consider the fundamental problem of matching a template to a signal. We do so by M-estimation, which encompasses procedures that are robust to gross errors (i.e., outliers). Using standard results from empirical process theory, we derive the convergence rate and the asymptotic distribution of the M-estimator under relatively mild assumptions. We also discuss the optimality of the estimator, both in finite samples in the minimax sense and in the large-sample limit in terms of local minimaxity and relative efficiency. Although most of the paper is dedicated to the study of the basic shift model in the context of a random design, we consider many extensions towards the end of the paper, including more flexible templates, fixed designs, the agnostic setting, and more.

Author

Arias-Castro, Ery; Zheng, Lin

Published

2020

Language

EN