- 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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