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Robust estimation ofk-component univariate normal mixtures
Authors:B R Clarke  C R Heathcote
Institution:(1) School of Mathematical and Physical Sciences, Murdoch University, 6150, Western Australia, Australia;(2) Department of Statistics, Australian National University, GPO Box 4, 2601 Canberra ACT, Australia
Abstract:The estimating equations derived from minimising aL 2 distance between the empirical distribution function and the parametric distribution representing a mixture ofk normal distributions with possibly different means and/or different dispersion parameters are given explicitly. The equations are of theM estimator form in which the psgr function is smooth, bounded and has bounded partial derivatives. As a consequence it is shown that there is a solution of the equations which is robust. In particular there exists a weakly continuous, Fréchet differentiable root and hence there is a consistent root of the equations which is asymptotically normal. These estimating equations offer a robust alternative to the maximum likelihood equations, which are known to yield nonrobust estimators.
Keywords:Influence function  weak continuity  mixtures of normals  Fré  cht differentiability  consistency  asymptotic normality  selection functional  minimum distance estimator
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