Asymptotic properties of statistical estimators using multivariate chi-squared measurements

Title: Asymptotic properties of statistical estimators using multivariate chi-squared measurements
Creator: Marelli, Damián; Fu, Minyue
Relation: Digital Signal Processing Vol. 103, Issue August 2020, no. 102754
Publisher Link: http://dx.doi.org/10.1016/j.dsp.2020.102754
Publisher: Academic Press
Resource Type: journal article
Date: 2020
Description: This paper studies the problem of estimating a parameter vector from measurements having a multivariate chi-squared distribution. Maximum likelihood estimation in this setting is unfeasible because the multivariate chi-squared distribution has no closed form expression. The typical approach to go around this consists in considering a sub-optimal solution by replacing the chi-squared distribution with a normal one. We investigate the theoretical properties of this approximation as the number of measurements approach infinity. More precisely, we show that this approximation is strongly consistency, asymptotically normal and asymptotically efficient. We consider a source localization problem as a case study.
Subject: asymptotic statistical properties; multivariate chi-squared distribution; parameter estimation; maximum likelihood estimation; Cramér-Rao lower bound
Identifier: http://hdl.handle.net/1959.13/1426373
Identifier: uon:38405
Identifier: ISSN:1051-2004
Language: eng
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