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MCMC for integer valued ARMA processes

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MCMC for integer valued ARMA processes. / Neal, Peter John; Subba Rao, Tata.

In: Journal of Time Series Analysis, Vol. 28, No. 1, 01.2007, p. 92-110.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Neal, PJ & Subba Rao, T 2007, 'MCMC for integer valued ARMA processes', Journal of Time Series Analysis, vol. 28, no. 1, pp. 92-110. https://doi.org/10.1111/j.1467-9892.2006.00500.x

APA

Neal, P. J., & Subba Rao, T. (2007). MCMC for integer valued ARMA processes. Journal of Time Series Analysis, 28(1), 92-110. https://doi.org/10.1111/j.1467-9892.2006.00500.x

Vancouver

Neal PJ, Subba Rao T. MCMC for integer valued ARMA processes. Journal of Time Series Analysis. 2007 Jan;28(1):92-110. https://doi.org/10.1111/j.1467-9892.2006.00500.x

Author

Neal, Peter John ; Subba Rao, Tata. / MCMC for integer valued ARMA processes. In: Journal of Time Series Analysis. 2007 ; Vol. 28, No. 1. pp. 92-110.

Bibtex

@article{9acc780e687740678fca177911229683,
title = "MCMC for integer valued ARMA processes",
abstract = "The classical statistical inference for integer-valued time-series has primarily been restricted to the integer-valued autoregressive (INAR) process. Markov chain Monte Carlo (MCMC) methods have been shown to be a useful tool in many branches of statistics and is particularly well suited to integer-valued time-series where statistical inference is greatly assisted by data augmentation. Thus in this article, we outline an efficient MCMC algorithm for a wide class of integer-valued autoregressive moving-average (INARMA) processes. Furthermore, we consider noise corrupted integer-valued processes and also models with change points. Finally, in order to assess the MCMC algorithms inferential and predictive capabilities we use a range of simulated and real data sets.",
keywords = "Integer-valued time-series;, MCMC, count data",
author = "Neal, {Peter John} and {Subba Rao}, Tata",
year = "2007",
month = jan,
doi = "10.1111/j.1467-9892.2006.00500.x",
language = "English",
volume = "28",
pages = "92--110",
journal = "Journal of Time Series Analysis",
issn = "0143-9782",
publisher = "Wiley-Blackwell",
number = "1",

}

RIS

TY - JOUR

T1 - MCMC for integer valued ARMA processes

AU - Neal, Peter John

AU - Subba Rao, Tata

PY - 2007/1

Y1 - 2007/1

N2 - The classical statistical inference for integer-valued time-series has primarily been restricted to the integer-valued autoregressive (INAR) process. Markov chain Monte Carlo (MCMC) methods have been shown to be a useful tool in many branches of statistics and is particularly well suited to integer-valued time-series where statistical inference is greatly assisted by data augmentation. Thus in this article, we outline an efficient MCMC algorithm for a wide class of integer-valued autoregressive moving-average (INARMA) processes. Furthermore, we consider noise corrupted integer-valued processes and also models with change points. Finally, in order to assess the MCMC algorithms inferential and predictive capabilities we use a range of simulated and real data sets.

AB - The classical statistical inference for integer-valued time-series has primarily been restricted to the integer-valued autoregressive (INAR) process. Markov chain Monte Carlo (MCMC) methods have been shown to be a useful tool in many branches of statistics and is particularly well suited to integer-valued time-series where statistical inference is greatly assisted by data augmentation. Thus in this article, we outline an efficient MCMC algorithm for a wide class of integer-valued autoregressive moving-average (INARMA) processes. Furthermore, we consider noise corrupted integer-valued processes and also models with change points. Finally, in order to assess the MCMC algorithms inferential and predictive capabilities we use a range of simulated and real data sets.

KW - Integer-valued time-series;

KW - MCMC

KW - count data

U2 - 10.1111/j.1467-9892.2006.00500.x

DO - 10.1111/j.1467-9892.2006.00500.x

M3 - Journal article

VL - 28

SP - 92

EP - 110

JO - Journal of Time Series Analysis

JF - Journal of Time Series Analysis

SN - 0143-9782

IS - 1

ER -