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

Kód výsledku v 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>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

<a href="/cs/project/GP201%2F09%2FP077" target="_blank" >GP201/09/P077: Vícerozměrné longitudinální modely k predikci kategoriální odezvy</a><br>

Návaznosti

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

Rok uplatnění

2009

Kód důvěrnosti údajů

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

Název periodika

Computational Statistics and Data Analysis

ISSN

0167-9473

e-ISSN

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Svazek periodika

53

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

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Kód UT WoS článku

000270624600004

EID výsledku v databázi Scopus

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