BAYESIAN ANALYSIS OF AUTOREGRESSIVE TIME SERIES VIA THE GIBBS SAMPLER

Robert E. McCulloch; Ruey S. Tsay

doi:10.1111/j.1467-9892.1994.tb00188.x

BAYESIAN ANALYSIS OF AUTOREGRESSIVE TIME SERIES VIA THE GIBBS SAMPLER

Robert E. McCulloch, Ruey S. Tsay

Research output: Contribution to journal › Article › peer-review

104 Scopus citations

Abstract

Abstract. Applications of the Gibbs Sampler in time series analysis are considered. We show that the sampler applies nicely to various problems in analyzing autoregressive processes and, in many cases, it enjoys certain advantages over the traditional methods. The problems considered include random level‐shift models, outliers and missing values. Real examples are used to illustrate the analysis.

Original language	English (US)
Pages (from-to)	235-250
Number of pages	16
Journal	Journal of Time Series Analysis
Volume	15
Issue number	2
DOIs	https://doi.org/10.1111/j.1467-9892.1994.tb00188.x
State	Published - Mar 1994
Externally published	Yes

Keywords

Missing value
outlier
posterior distribution
random level‐shift model

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty
Applied Mathematics

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10.1111/j.1467-9892.1994.tb00188.x

Cite this

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abstract = "Abstract. Applications of the Gibbs Sampler in time series analysis are considered. We show that the sampler applies nicely to various problems in analyzing autoregressive processes and, in many cases, it enjoys certain advantages over the traditional methods. The problems considered include random level‐shift models, outliers and missing values. Real examples are used to illustrate the analysis.",

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N2 - Abstract. Applications of the Gibbs Sampler in time series analysis are considered. We show that the sampler applies nicely to various problems in analyzing autoregressive processes and, in many cases, it enjoys certain advantages over the traditional methods. The problems considered include random level‐shift models, outliers and missing values. Real examples are used to illustrate the analysis.

AB - Abstract. Applications of the Gibbs Sampler in time series analysis are considered. We show that the sampler applies nicely to various problems in analyzing autoregressive processes and, in many cases, it enjoys certain advantages over the traditional methods. The problems considered include random level‐shift models, outliers and missing values. Real examples are used to illustrate the analysis.

KW - Missing value

KW - outlier

KW - posterior distribution

KW - random level‐shift model

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BAYESIAN ANALYSIS OF AUTOREGRESSIVE TIME SERIES VIA THE GIBBS SAMPLER

Abstract

Keywords

ASJC Scopus subject areas

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