• Corpus ID: 248693653

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

  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},
Robust estimation under multivariate normal (MVN) mixture model is always a computational challenge. A recently proposed maximum pseudo β -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 β -likelihood… 



Robust Clustering with Normal Mixture Models: A Pseudo β-Likelihood Approach

A robust alternative to the ordinary likelihood approach for this estimation problem which performs simultaneous estimation and data clustering and leads to subsequent anomaly detection and is seen to perform competitively or better compared to the popular existing methods.

Robust and efficient estimation by minimising a density power divergence

A minimum divergence estimation method is developed for robust parameter estimation. The proposed approach uses new density-based divergences which, unlike existing methods of this type such as

Robust estimation in the normal mixture model

Introduction to Empirical Processes and Semiparametric Inference

Overview.- An Overview of Empirical Processes.- Overview of Semiparametric Inference.- Case Studies I.- Empirical Processes.- to Empirical Processes.- Preliminaries for Empirical Processes.-

Finite Mixture Models

  • P. Deb
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    Encyclopedia of Autism Spectrum Disorders
  • 2021
Finite mixture models provide a natural way of modeling continuous or discrete outcomes that are observed from populations consisting of a finite number of homogeneous subpopulations. Applications of

Statistical Inference: The Minimum Distance Approach

Introduction General Notation Illustrative Examples Some Background and Relevant Definitions Parametric Inference based on the Maximum Likelihood Method Hypothesis Testing by Likelihood Methods

Trimmed $k$-means: an attempt to robustify quantizers

A class of procedures based on impartial trimming (self-determined by the data) is introduced with the aim of robustifying k-means, hence the associated clustering analysis. We include a detailed

Weak Convergence and Empirical Processes: With Applications to Statistics

This chapter discusses Convergence: Weak, Almost Uniform, and in Probability, which focuses on the part of Convergence of the Donsker Property which is concerned with Uniformity and Metrization.

A general trimming approach to robust Cluster Analysis

We introduce a new method for performing clustering with the aim of fitting clusters with different scatters and weights. It is designed by allowing to handle a proportion $\alpha$ of contaminating

High Dimensional Probability II (Vol. 47)

  • 2012