Efficient MCMC estimation of some elliptical copula regression models through scale mixtures of normals

2018 ◽  
Vol 35 (3) ◽  
pp. 808-822
Author(s):  
Nuttanan Wichitaksorn ◽  
Richard Gerlach ◽  
S.T. Boris Choy
Keyword(s):  
Regression Models ◽  
Scale Mixtures ◽  
Mcmc Estimation ◽  
Scale Mixtures Of Normals ◽  
Elliptical Copula

scholarly journals Using Scale Mixtures Of Normals To Model Continuously Compounded Returns

2005 ◽  
Vol 4 (1) ◽  
pp. 214-226
Author(s):  
Hasan Hamdan ◽  
John Nolan ◽  
Melanie Wilson ◽  
Kristen Dardia
Keyword(s):  
Scale Mixtures ◽  
Scale Mixtures Of Normals

Linear censored regression models with scale mixtures of normal distributions

2015 ◽  
Vol 58 (1) ◽  
pp. 247-278 ◽  
Author(s):  
Aldo M. Garay ◽  
Victor H. Lachos ◽  
Heleno Bolfarine ◽  
Celso R. B. Cabral
Keyword(s):  
Regression Models ◽  
Censored Regression ◽  
Normal Distributions ◽  
Scale Mixtures ◽  
Censored Regression Models ◽  
Mixtures Of Normal Distributions

Scale mixtures log-Birnbaum–Saunders regression models with censored data: a Bayesian approach

2017 ◽  
Vol 87 (10) ◽  
pp. 2002-2022 ◽  
Author(s):  
Victor H. Lachos ◽  
Dipak K. Dey ◽  
Vicente G. Cancho ◽  
Francisco Louzada
Keyword(s):  
Censored Data ◽  
Bayesian Approach ◽  
Regression Models ◽  
Scale Mixtures

A Bayesian approach on the two-piece scale mixtures of normal homoscedastic nonlinear regression models

2020 ◽  
pp. 1-18
Author(s):  
Zahra Barkhordar ◽  
Mohsen Maleki ◽  
Zahra Khodadadi ◽  
Darren Wraith ◽  
Farajollah Negahdari
Keyword(s):  
Bayesian Approach ◽  
Nonlinear Regression ◽  
Regression Models ◽  
Nonlinear Regression Models ◽  
Scale Mixtures

scholarly journals BAYESIAN REGRESSION ANALYSIS WITH SCALE MIXTURES OF NORMALS

2000 ◽  
Vol 16 (1) ◽  
pp. 80-101 ◽  
Author(s):  
Carmen Fernández ◽  
Mark F.J. Steel
Keyword(s):  
Bayesian Regression ◽  
Design Matrix ◽  
Mixing Distribution ◽  
Scale Parameters ◽  
Common Reference ◽  
Scale Mixtures ◽  
Wide Range ◽  
Under Sampling ◽  
Scale Mixtures Of Normals ◽  
Posterior Moments

This paper considers a Bayesian analysis of the linear regression model under independent sampling from general scale mixtures of normals. Using a common reference prior, we investigate the validity of Bayesian inference and the existence of posterior moments of the regression and scale parameters. We find that whereas existence of the posterior distribution does not depend on the choice of the design matrix or the mixing distribution, both of them can crucially intervene in the existence of posterior moments. We identify some useful characteristics that allow for an easy verification of the existence of a wide range of moments. In addition, we provide full characterizations under sampling from finite mixtures of normals, Pearson VII, or certain modulated normal distributions. For empirical applications, a numerical implementation based on the Gibbs sampler is recommended.

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