Bayesian analysis in multivariate regression models with conjugate priors

Arashi, Mohammad; Iranmanesh, Anis; Norouzirad, Mina; Jenatabadi, Hashem Salarzadeh

doi:10.1080/02331888.2013.809720

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Normal-inverse Wishart Prior1

Introduction1

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2014

2023

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Cited by 4 publications

(2 citation statements)

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“…. Following Arashi et al (2014), the conjugate prior for the matrix variate elliptical model can be obtained as…”

Section: Normal-inverse Wishart Priormentioning

confidence: 99%

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Estimation under the matrix variate elliptical model

Niekerk

Bekker

Arashi

et al. 2016

sasj

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The problem of estimation within the matrix variate elliptical model is addressed. In this paper a subjective Bayesian approach is followed to derive new estimators for the parameters of the matrix variate elliptical model by assuming the previously intractable normal-Wishart prior. These new estimators are compared to the estimators derived under a normal-inverse Wishart prior as well as the objective Jeffreys’ prior which results in the maximum likelihood estimators, using different measures. A valuable contribution is the development of algorithms for the simulation of the posterior distributions of the matrix variate parameters with emphasis on the new proposed estimators. A simulation study as well as Fisher’s Iris data set are used to illustrate the novelty of these new estimators and to investigate the accuracy gained by assuming the normal-Wishart prior.

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“…. Following Arashi et al (2014), the conjugate prior for the matrix variate elliptical model can be obtained as…”

Section: Normal-inverse Wishart Priormentioning

confidence: 99%

“…Note that w(z) is identified from the inverse Laplace transform of the density function of the particular elliptical model. See Arashi, Iranmanesh, Norouzirad and Salarzadeh-Jenatabadi (2014) and Arashi, Saleh and Tabatabaey (2013) for more details.…”

Section: Introductionmentioning

confidence: 99%