Bayesian forecasting in univariate autoregressive models with normal-gamma prior distribution of unknown parameters

Igor Vladimirov; Bevan Thompson

Bayesian forecasting in univariate autoregressive models with normal-gamma prior distribution of unknown parameters

Igor Vladimirov
, Bevan Thompson

School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › peer-review

Abstract

We consider the problem of computing the mean-square optimal Bayesian predictor in
univariate autoregressive models with Gaussian innovations. The unknown coefficients
of the model are ascribed a normal-gamma prior distribution providing a family of con-
jugate priors. The problem is reduced to calculating the state-space realization matrices of
an iterated linear discrete time-invariant system. The system theoretic solution employs a
scalarization technique for computing the power moments of Gaussian random matrices
developed recently by the authors.

Original language	English
Title of host publication	Proceedings of the conference on Differential & Difference Equations and Applications
Publisher	Hindawi Publishing Corporation
Pages	1099-1108
Number of pages	10
ISBN (Electronic)	977-5945-380
Publication status	Published - 2006

Cite this

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title = "Bayesian forecasting in univariate autoregressive models with normal-gamma prior distribution of unknown parameters",

abstract = "We consider the problem of computing the mean-square optimal Bayesian predictor in univariate autoregressive models with Gaussian innovations. The unknown coefficients of the model are ascribed a normal-gamma prior distribution providing a family of con- jugate priors. The problem is reduced to calculating the state-space realization matrices of an iterated linear discrete time-invariant system. The system theoretic solution employs a scalarization technique for computing the power moments of Gaussian random matrices developed recently by the authors.",

author = "Igor Vladimirov and Bevan Thompson",

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Bayesian forecasting in univariate autoregressive models with normal-gamma prior distribution of unknown parameters. / Vladimirov, Igor; Thompson, Bevan.
Proceedings of the conference on Differential & Difference Equations and Applications. Hindawi Publishing Corporation, 2006. p. 1099-1108.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › peer-review

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AU - Vladimirov, Igor

AU - Thompson, Bevan

N1 - Proceedings of the Conference on Differential & Difference Equations and Applications, 1-5 August 2005, Florida Institute of Technology (FIT), Melbourne, Florida, USA, Eds. R.P. Agarwal and K.Perera

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N2 - We consider the problem of computing the mean-square optimal Bayesian predictor in univariate autoregressive models with Gaussian innovations. The unknown coefficients of the model are ascribed a normal-gamma prior distribution providing a family of con- jugate priors. The problem is reduced to calculating the state-space realization matrices of an iterated linear discrete time-invariant system. The system theoretic solution employs a scalarization technique for computing the power moments of Gaussian random matrices developed recently by the authors.

AB - We consider the problem of computing the mean-square optimal Bayesian predictor in univariate autoregressive models with Gaussian innovations. The unknown coefficients of the model are ascribed a normal-gamma prior distribution providing a family of con- jugate priors. The problem is reduced to calculating the state-space realization matrices of an iterated linear discrete time-invariant system. The system theoretic solution employs a scalarization technique for computing the power moments of Gaussian random matrices developed recently by the authors.

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BT - Proceedings of the conference on Differential & Difference Equations and Applications

PB - Hindawi Publishing Corporation

ER -

Bayesian forecasting in univariate autoregressive models with normal-gamma prior distribution of unknown parameters

Abstract

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