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

Content Provider	Europe PMC
Author	da Silva Ferreira, Clécio Lachos, Víctor H. Garay, Aldo M.
Copyright Year	2019
Abstract	ABSTRACT 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.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC9041946&blobtype=pdf
Page Count	30
ISSN	02664763
Volume Number	47
DOI	10.1080/02664763.2019.1691158
PubMed Central reference number	PMC9041946
Issue Number	9
PubMed reference number	35707586
Journal	Journal of Applied Statistics [J Appl Stat]
e-ISSN	13600532
Language	English
Publisher	Taylor & Francis
Publisher Date	2019-11-11
Access Restriction	Open
Rights License	© 2019 Informa UK Limited, trading as Taylor & Francis Group
Subject Keyword	EM algorithm heteroscedastic nonlinear regression models influence diagnostics likelihood ratio test skew scale mixtures of normal distributions
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty