Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/87916
Type: Artigo de periódico
Title: Influence Diagnostics For Student-t Censored Linear Regression Models
Author: Massuia M.B.
Cabral C.R.B.
Matos L.A.
Lachos V.H.
Abstract: In this paper, we extend the censored linear regression model with normal errors to Student-t errors. A simple EM-type algorithm for iteratively computing maximum-likelihood estimates of the parameters is presented. To examine the performance of the proposed model, case-deletion and local influence techniques are developed to show its robust aspect against outlying and influential observations. This is done by the analysis of the sensitivity of the EM estimates under some usual perturbation schemes in the model or data and by inspecting some proposed diagnostic graphics. The efficacy of the method is verified through the analysis of simulated data sets and modelling a real data set first analysed under normal errors. The proposed algorithm and methods are implemented in the R package CensRegMod.
Editor: Taylor and Francis Ltd.
Rights: fechado
Identifier DOI: 10.1080/02331888.2014.958489
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-84907900307&partnerID=40&md5=4258030d573de7a03921fe4637127ba8
Date Issue: 2014
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-84907900307.pdf218.09 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.