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A new R package for Bayesian estimation of multivariate normal mixtures allowing for selection of the number of components and interval-censored data

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F09%3A00206601" target="_blank" >RIV/00216208:11320/09:00206601 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    A new R package for Bayesian estimation of multivariate normal mixtures allowing for selection of the number of components and interval-censored data

  • Original language description

    An R package mixAK is introduced which implements routines for a semiparametric density estimation through normal mixtures using the Markov chain Monte Carlo (MCMC) methodology. Besides producing the MCMC output, the package computes posterior summary statistics for important characteristics of the fitted distribution or computes and visualizes the posterior predictive density. For the estimated models, penalized expected deviance (PED) and deviance information criterion (DIC) is directly computed whichallows for a selection of mixture components. Additionally, multivariate right-, left- and interval-censored observations are allowed. For univariate problems, the reversible jump MCMC algorithm has been implemented and can be used for a joint estimation of the mixture parameters and the number of mixture components. We briefly review implemented algorithms and illustrate the use of the package on three real examples of different complexity.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    BB - Applied statistics, operational research

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GP201%2F09%2FP077" target="_blank" >GP201/09/P077: Multivariate longitudinal models to predict a categorical outcome</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Others

  • Publication year

    2009

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Computational Statistics and Data Analysis

  • ISSN

    0167-9473

  • e-ISSN

  • Volume of the periodical

    53

  • Issue of the periodical within the volume

    12

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    16

  • Pages from-to

  • UT code for WoS article

    000270624600004

  • EID of the result in the Scopus database