A bayesian analysis of multiple changes in the variance of first
- order autoregressive time series models

Venkatesan, D; Gallo, Michele; Vijayakumar, M.

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The problem of a change in the mean of a sequence of random variables at an unknown time point has been addressed extensively in the literature. But, the problem of a change in the variance at an unknown time point has, however, been covered less widely. This paper analyses a sequence of first order autoregressive time series model in which the variance may have subjected to multiple changes at an unknown time points. Posterior distributions are found both for the unknown points of time at which the changes occurred and for the parameters of the model. A numerical example is illustrated.

Titolo:	A bayesian analysis of multiple changes in the variance of first - order autoregressive time series models
Autori:	VENKATESAN D GALLO, Michele VIJAYAKUMAR M. mostra contributor esterni nascondi contributor esterni
Data di pubblicazione:	2012
Rivista:	NEURAL, PARALLEL & SCIENTIFIC COMPUTATIONS.
Abstract:	The problem of a change in the mean of a sequence of random variables at an unknown time point has been addressed extensively in the literature. But, the problem of a change in the variance at an unknown time point has, however, been covered less widely. This paper analyses a sequence of first order autoregressive time series model in which the variance may have subjected to multiple changes at an unknown time points. Posterior distributions are found both for the unknown points of time at which the changes occurred and for the parameters of the model. A numerical example is illustrated.
Handle:	http://hdl.handle.net/11574/37408
Appare nelle tipologie:	1.1 Articolo in rivista

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