Robust High-Dimensional Modeling with the Contaminated Gaussian Distribution

No abstract is available for this paper.

• Publication year

  2014

• Venue

  arXiv: Methodology

• Publication date

  2014-08-09

• Fields of study

  Mathematics, Computer Science, Engineering

• Identifiers

  No identifiers available.

• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• The proof and measurement of association between two things.

  2015cited by this paper
• Robust Clustering via Parsimonious Mixtures of Contaminated Gaussian Distributions

  2013cited by this paper
• Finite mixtures of unimodal beta and gamma densities and the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k$$\end{d

  2012cited by this paper
• Clustering and classification via cluster-weighted factor analyzers

  2012cited by this paper
• Maximum likelihood estimation of mixtures of factor analyzers

  2011cited by this paper
• Latent Variable Models and Factor Analysis: A Unified Approach

  2011cited by this paper
• Fast ML Estimation for the Mixture of Factor Analyzers via an ECM Algorithm

  2008cited by this paper
• Parsimonious Gaussian mixture models

  2008cited by this paper
• Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution

  2007cited by this paper
• Multivariate T-Distributions and Their Applications

  2004cited by this paper
• Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models

  2003cited by this paper
• Modelling high-dimensional data by mixtures of factor analyzers

  2003cited by this paper
• The EM algorithm and related statistical models

  2003cited by this paper
• Choosing initial values for the EM algorithm for finite mixtures

  2003cited by this paper
• Fixed Point Clusters for Linear Regression: Computation and Comparison

  2002cited by this paper
• Finite Mixture Models

  2000cited by this paper
• Robust mixture modelling using the t distribution

  2000cited by this paper
• The EM Algorithm—an Old Folk‐song Sung to a Fast New Tune

  1997cited by this paper
• A First Course in Multivariate Statistics

  1997cited by this paper
• Gaussian parsimonious clustering models

  1995cited by this paper
• The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence

  1994cited by this paper
• The distribution of the likelihood ratio for mixtures of densities from the one-parameter exponential family

  1994cited by this paper
• Maximum likelihood estimation via the ECM algorithm: A general framework

  1993cited by this paper
• Robust Statistical Modeling Using the t Distribution

  1989cited by this paper
• On the theory of elliptically contoured distributions

  1981cited by this paper
• Mixture Models, Outliers, and the EM Algorithm

  1980cited by this paper
• Estimating the Dimension of a Model

  1978cited by this paper
• Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper

  1977cited by this paper
• A Simplex Method for Function Minimization

  1965cited by this paper
• XXV.—On Bernoulli's Numerical Solution of Algebraic Equations

  year unknowncited by this paper

• Multiple scaled contaminated normal distribution and its application in clustering

  2018cites this paper
• Robust Clustering in Regression Analysis via the Contaminated Gaussian Cluster-Weighted Model

  2017cites this paper
• The multivariate leptokurtic‐normal distribution and its application in model‐based clustering

  2017cites this paper
• An Efficient Implementation of a Robust Clustering Algorithm

  2016influential citation
• ContaminatedMixt: An R Package for Fitting Parsimonious Mixtures of Multivariate Contaminated Normal Distributions

  2016cites this paper
• Clustering Multivariate Longitudinal Observations: The Contaminated Gaussian Hidden Markov Model

  2016cites this paper
• Model-Based Clustering

  2016cites this paper
• Asymmetric clusters and outliers: Mixtures of multivariate contaminated shifted asymmetric Laplace distributions

  2014cites this paper
• Robust Clustering in Regression Analysis via the Contaminated Gaussian Cluster-Weighted Model

  2014influential citation