Existence and Consistency of the Maximum Pseudo \b{eta}-Likelihood Estimators for Multivariate Normal Mixture Models

Autor:	Chakraborty, Soumya, Basu, Ayanendranath, Ghosh, Abhik
Rok vydání:	2022
Předmět:	Mathematics - Statistics Theory
Druh dokumentu:	Working Paper
Popis:	Robust estimation under multivariate normal (MVN) mixture model is always a computational challenge. A recently proposed maximum pseudo \b{eta}-likelihood estimator aims to estimate the unknown parameters of a MVN mixture model in the spirit of minimum density power divergence (DPD) methodology but with a relatively simpler and tractable computational algorithm even for larger dimensions. In this letter, we will rigorously derive the existence and weak consistency of the maximum pseudo \b{eta}-likelihood estimator in case of MVN mixture models under a reasonable set of assumptions. Comment: arXiv admin note: substantial text overlap with arXiv:2009.04710
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.05405 Zobrazit plný text záznamu View this record from Arxiv