Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/55354
Type: Artigo
Title: Bayesian modeling of autoregressive partial linear models with scale mixture of normal errors
Author: Ferreira, Guillermo
Castro, Luis M.
Lachos, Victor H.
Dias, Ronaldo
Abstract: Normality and independence of error terms are typical assumptions for partial linear models. However, these assumptions may be unrealistic in many fields, such as economics, finance and biostatistics. In this paper, a Bayesian analysis for partial linear model with first-order autoregressive errors belonging to the class of the scale mixtures of normal distributions is studied in detail. The proposed model provides a useful generalization of the symmetrical linear regression model with independent errors, since the distribution of the error term covers both correlated and thick-tailed distributions, and has a convenient hierarchical representation allowing easy implementation of a Markov chain Monte Carlo scheme. In order to examine the robustness of the model against outlying and influential observations, a Bayesian case deletion influence diagnostics based on the Kullback-Leibler (K-L) divergence is presented. The proposed method is applied to monthly and daily returns of two Chilean companies.
Normality and independence of error terms are typical assumptions for partial linear models. However, these assumptions may be unrealistic in many fields, such as economics, finance and biostatistics. In this paper, a Bayesian analysis for partial linear
Subject: Auto-regressão (Estatística)
Observações censuradas (Estatística)
Influência local (Estatística)
Modelos lineares parciais
Misturas de escala (Estatística)
Country: Reino Unido
Editor: Taylor & Francis
Citation: Journal Of Applied Statistics. Taylor & Francis Ltd, v. 40, n. 8, n. 1796, n. 1816, 2013.
Rights: Fechado
Identifier DOI: 10.1080/02664763.2013.796349
Address: https://www.tandfonline.com/doi/full/10.1080/02664763.2013.796349
Date Issue: 2013
Appears in Collections:IMECC - Artigos e Outros Documentos

Files in This Item:
File SizeFormat 
000321609800012.pdf594.44 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.