Bayesian wavelet analysis of autoregressive fractionally integrated moving-average processes

2006 ◽  
Vol 136 (10) ◽  
pp. 3415-3434 ◽  
Author(s):  
Kyungduk Ko ◽  
Marina Vannucci
Keyword(s):  
Wavelet Analysis ◽  
Moving Average ◽  
Moving Average Processes ◽  
Bayesian Wavelet

The distributional structure of finite moving-average processes

1988 ◽  
Vol 25 (02) ◽  
pp. 313-321 ◽  
Author(s):  
ED McKenzie
Keyword(s):  
Moving Average ◽  
Markov Property ◽  
Covariance Structure ◽  
Time Reversibility ◽  
Linear Processes ◽  
Joint Distributions ◽  
Moving Average Processes ◽  
Standard Models ◽  
Non Gaussian ◽  
Analysis Of Time Series

Analysis of time-series models has, in the past, concentrated mainly on second-order properties, i.e. the covariance structure. Recent interest in non-Gaussian and non-linear processes has necessitated exploration of more general properties, even for standard models. We demonstrate that the powerful Markov property which greatly simplifies the distributional structure of finite autoregressions has an analogue in the (non-Markovian) finite moving-average processes. In fact, all the joint distributions of samples of a qth-order moving average may be constructed from only the (q + 1)th-order distribution. The usefulness of this result is illustrated by references to three areas of application: time-reversibility; asymptotic behaviour; and sums and associated point and count processes. Generalizations of the result are also considered.

Bayesian Wavelet Analysis Using Nonlocal Priors with an Application to fMRI Analysis

Sankhya B ◽  
2017 ◽  
Vol 79 (2) ◽  
pp. 361-388 ◽  
Author(s):  
Nilotpal Sanyal ◽  
Marco A. R. Ferreira
Keyword(s):  
Wavelet Analysis ◽  
Fmri Analysis ◽  
Bayesian Wavelet
Sign in / Sign up

Export Citation Format

Share Document