Consistency of M-estimators of nonlinear signal processing models

Title: Consistency of M-estimators of nonlinear signal processing models
Creator: Mahata, Kaushik; Mitra, Amit; Mitra, Sharmishtha
Relation: ARC.DP130103909
Relation: Statistical Methodology Vol. 28, Issue January 2016, p. 18-36
Publisher Link: http://dx.doi.org/10.1016/j.stamet.2015.07.004
Publisher: Elsevier
Resource Type: journal article
Date: 2016
Description: In this paper, we consider the problem of robust M-estimation of parameters of nonlinear signal processing models. We investigate the conditions under which estimators are strongly consistent for convex and non-convex penalty functions and a wide class of noise scenarios, contaminating the actual transmitted signal. It is shown that the M-estimators of a general nonlinear signal model are asymptotically consistent with probability one under different sets of sufficient conditions on loss function and noise distribution. Simulations are performed for nonlinear superimposed sinusoidal model to observe the small sample performance of the M-estimators for various heavy tailed error distributions, outlier contamination levels and sample sizes.
Subject: asymptotic analysis; M-estimator; nonlinear signal processing model; robust statistics; strong consistency
Identifier: http://hdl.handle.net/1959.13/1319891
Identifier: uon:24000
Identifier: ISSN:1572-3127
Language: eng
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