dc.creatorGARAY, Aldo M.
dc.creatorHASHIMOTO, Elizabeth M.
dc.creatorORTEGA, Edwin M. M.
dc.creatorLACHOS, Victor H.
dc.date.accessioned2012-10-19T02:22:11Z
dc.date.accessioned2018-07-04T14:52:55Z
dc.date.available2012-10-19T02:22:11Z
dc.date.available2018-07-04T14:52:55Z
dc.date.created2012-10-19T02:22:11Z
dc.date.issued2011
dc.identifierCOMPUTATIONAL STATISTICS & DATA ANALYSIS, v.55, n.3, p.1304-1318, 2011
dc.identifier0167-9473
dc.identifierhttp://producao.usp.br/handle/BDPI/18955
dc.identifier10.1016/j.csda.2010.09.019
dc.identifierhttp://dx.doi.org/10.1016/j.csda.2010.09.019
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1615746
dc.description.abstractThe zero-inflated negative binomial model is used to account for overdispersion detected in data that are initially analyzed under the zero-Inflated Poisson model A frequentist analysis a jackknife estimator and a non-parametric bootstrap for parameter estimation of zero-inflated negative binomial regression models are considered In addition an EM-type algorithm is developed for performing maximum likelihood estimation Then the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and some ways to perform global influence analysis are derived In order to study departures from the error assumption as well as the presence of outliers residual analysis based on the standardized Pearson residuals is discussed The relevance of the approach is illustrated with a real data set where It is shown that zero-inflated negative binomial regression models seems to fit the data better than the Poisson counterpart (C) 2010 Elsevier B V All rights reserved
dc.languageeng
dc.publisherELSEVIER SCIENCE BV
dc.relationComputational Statistics & Data Analysis
dc.rightsCopyright ELSEVIER SCIENCE BV
dc.rightsrestrictedAccess
dc.subjectBootstrap
dc.subjectEM algorithm
dc.subjectGlobal influence
dc.subjectLocal influence
dc.subjectNegative binomial distribution
dc.subjectZero inflated models
dc.titleOn estimation and influence diagnostics for zero-inflated negative binomial regression models
dc.typeArtículos de revistas


Este ítem pertenece a la siguiente institución