Influence diagnostics in heteroscedastic and/or autoregressive nonlinear elliptical models for correlated data
Contribuinte(s) |
UNIVERSIDADE DE SÃO PAULO |
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Data(s) |
02/10/2013
02/10/2013
2012
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Resumo |
In this paper, we propose nonlinear elliptical models for correlated data with heteroscedastic and/or autoregressive structures. Our aim is to extend the models proposed by Russo et al. [22] by considering a more sophisticated scale structure to deal with variations in data dispersion and/or a possible autocorrelation among measurements taken throughout the same experimental unit. Moreover, to avoid the possible influence of outlying observations or to take into account the non-normal symmetric tails of the data, we assume elliptical contours for the joint distribution of random effects and errors, which allows us to attribute different weights to the observations. We propose an iterative algorithm to obtain the maximum-likelihood estimates for the parameters and derive the local influence curvatures for some specific perturbation schemes. The motivation for this work comes from a pharmacokinetic indomethacin data set, which was analysed previously by Bocheng and Xuping [1] under normality. FAPESP FAPESP FACEPE FACEPE CNPq (Brazil) CNPq, Brazil |
Identificador |
JOURNAL OF APPLIED STATISTICS, v. 39, n. 5, pp. 1049-1067, JUN, 2012 0266-4763 http://www.producao.usp.br/handle/BDPI/33962 10.1080/02664763.2011.636030 |
Idioma(s) |
eng |
Publicador |
TAYLOR & FRANCIS LTD ABINGDON |
Relação |
JOURNAL OF APPLIED STATISTICS |
Direitos |
restrictedAccess Copyright TAYLOR & FRANCIS LTD |
Palavras-Chave | #AUTOREGRESSIVE STRUCTURE #CORRELATED DATA #ELLIPTICAL DISTRIBUTIONS #HETEROSCEDASTIC MODELS #NONLINEAR MODELS #LINEAR-MODELS #LOCAL INFLUENCE #LIKELIHOOD #ERRORS #STATISTICS & PROBABILITY |
Tipo |
article original article publishedVersion |