Existence and Consistency of the Maximum Pseudo \b{eta}-Likelihood Estimators for Multivariate Normal Mixture Models
Statistics Theory
2023-02-14 v2 Statistics Theory
Abstract
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.
Keywords
Cite
@article{arxiv.2205.05405,
title = {Existence and Consistency of the Maximum Pseudo \b{eta}-Likelihood Estimators for Multivariate Normal Mixture Models},
author = {Soumya Chakraborty and Ayanendranath Basu and Abhik Ghosh},
journal= {arXiv preprint arXiv:2205.05405},
year = {2023}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2009.04710