CONICET | Buscador de Institutos y Recursos Humanos

This paper studies the problem of estimating a parameter vector from measurements having amultivariate chi-squared distribution. Maximum likelihood estimation in this setting is unfeasible becausethe multivariate chi-squared distribution has no closed form expression. The typical approach to goaround this consists in considering a sub-optimal solution by replacing the chi-squared distributionwith a normal one. We investigate the theoretical properties of this approximation as the number ofmeasurements 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 casestudy.