Inference and diagnostics for heteroscedastic nonlinear regression models under skew scale mixtures of normal distributions.

Autor: da Silva Ferreira C; Department of Statistics, Federal University of Juiz de Fora, Juiz de Fora, Brazil., Lachos VH; Department of Statistics, University of Connecticut, Storrs, CT, USA., Garay AM; Department of Statistics, Federal University of Pernambuco, Recife, Brazil.
Jazyk: angličtina
Zdroj: Journal of applied statistics [J Appl Stat] 2019 Nov 11; Vol. 47 (9), pp. 1690-1719. Date of Electronic Publication: 2019 Nov 11 (Print Publication: 2020).
DOI: 10.1080/02664763.2019.1691158
Abstrakt: The heteroscedastic nonlinear regression model (HNLM) is an important tool in data modeling. In this paper we propose a HNLM considering skew scale mixtures of normal (SSMN) distributions, which allows fitting asymmetric and heavy-tailed data simultaneously. Maximum likelihood (ML) estimation is performed via the expectation-maximization (EM) algorithm. The observed information matrix is derived analytically to account for standard errors. In addition, diagnostic analysis is developed using case-deletion measures and the local influence approach. A simulation study is developed to verify the empirical distribution of the likelihood ratio statistic, the power of the homogeneity of variances test and a study for misspecification of the structure function. The method proposed is also illustrated by analyzing a real dataset.
Competing Interests: No potential conflict of interest was reported by the authors.
(© 2019 Informa UK Limited, trading as Taylor & Francis Group.)
Databáze: MEDLINE
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