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Type: Artigo de periódico
Title: Bayesian modeling of autoregressive partial linear models with scale mixture of normal errors
Author: Ferreira, G
Castro, LM
Lachos, VH
Dias, R
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.
Subject: autoregressive time series model
Gibbs sampler
influential observations
partial linear models
scale mixtures of normal distributions
Country: Inglaterra
Editor: Taylor & Francis Ltd
Rights: fechado
Identifier DOI: 10.1080/02664763.2013.796349
Date Issue: 2013
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

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