Identifiability and Comparison of Estimation Methods on Weibull Mixture Models

Autor:	Humberto Vaquera Huerta, José A. Villaseñor Alva, Eduardo Gutiérrez González, Olga Vladimirovna Panteleeva
Rok vydání:	2014
Předmět:	Statistics and Probability Modeling and Simulation Scoring algorithm Statistics Expectation–maximization algorithm Monte Carlo method Estimator Identifiability Akaike information criterion Mixture model Weibull distribution Mathematics
Zdroj:	Communications in Statistics - Simulation and Computation. 44:1879-1900
ISSN:	1532-4141 0361-0918
DOI:	10.1080/03610918.2013.839031
Popis:	In this article, the finite mixture model of Weibull distributions is studied, the identifiability of the model with m components is proven, and the parameter estimators for the case of two components resulted by several algorithms are compared. The parameter estimators are obtained with maximum likelihood performing calculations with different algorithms: expectation-maximization (EM), Fisher scoring, backfitting, optimization of k-nearest neighbor approach, and random walk algorithm using Monte Carlo simulation. The Akaike information criterion and the log-likelihood value are used to compare models. In general, the proposed random walk algorithm shows better performance in mean square error and bias. Finally, the results are applied to electronic component lifetime data.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::cd1fec9f24ff526aecc2c8486943e46b https://doi.org/10.1080/03610918.2013.839031 Zobrazit plný text záznamu