Influence diagnostics in heteroscedastic and/or autoregressive nonlinear elliptical models for correlated data


Autoria(s): Russo, Cibele M.; Paula, Gilberto A.; Cysneiros, Francisco Jose A.; Aoki, Reiko
Contribuinte(s)

UNIVERSIDADE DE SÃO PAULO

Data(s)

02/10/2013

02/10/2013

2012

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

http://dx.doi.org/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