Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/85206
Type: Artigo
Title: The sinh-normal/independent nonlinear regression model
Author: Vilca, Filidor
Zeller, Camila Borelli
Cordeiro, Gauss M.
Abstract: The normal/independent family of distributions is an attractive class of symmetric heavy-tailed density functions. They have a nice hierarchical representation to make inferences easily. We propose the Sinh-normal/independent distribution which extends the Sinh-normal (SN) distribution [23]. We discuss some of its properties and propose the Sinh-normal/independent nonlinear regression model based on a similar setup of Lemonte and Cordeiro [18], who applied the Birnbaum–Saunders distribution. We develop an EM-algorithm for maximum likelihood estimation of the model parameters. In order to examine the robustness of this flexible class against outlying observations, we perform a simulation study and analyze a real data set to illustrate the usefulness of the new model.
The normal/independent family of distributions is an attractive class of symmetric heavy-tailed density functions. They have a nice hierarchical representation to make inferences easily. We propose the Sinh-normal/independent distribution which extends the Sinh-normal (SN) distribution [23]. We discuss some of its properties and propose the Sinh-normal/independent nonlinear regression model based on a similar setup of Lemonte and Cordeiro [18], who applied the Birnbaum–Saunders distribution. We develop an EM-algorithm for maximum likelihood estimation of the model parameters. In order to examine the robustness of this flexible class against outlying observations, we perform a simulation study and analyze a real data set to illustrate the usefulness of the new model.
Subject: Modelos de regressão (Estatística)
Birnbaum-Saunders, Distribuição de
Algoritmos de esperança-maximização
Métodos estatísticos robustos
Distribuição (Probabilidades)
Country: Reino Unido
Editor: Taylor & Francis
Citation: Journal Of Applied Statistics. Taylor And Francis Ltd., v. , n. , p. - , 2015.
Rights: fechado
Identifier DOI: 10.1080/02664763.2015.1005059
Address: https://www.tandfonline.com/doi/abs/10.1080/02664763.2015.1005059
Date Issue: 2015
Appears in Collections:IMECC - Artigos e Outros Documentos

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