Research on Time Series and Change Points: Adrian Raftery

I am interested in non-standard time series analysis, in which the assumptions underlying, say, ARIMA or Kalman filter models break down. My interests are:
(1) Non-Gaussian time series, including time series on non-Euclidean spaces, such as time series of angles or compositions. I have been using the Mixture Transition Distribution (MTD) model in this context.
(2) Long-memory time series.
(3) Robust model selection in time series using Bayes factors.
(4) Change-point modeling.

Some Key References:

Le, N.D., Martin, R.D. and Raftery, A.E. (1996) Modeling outliers, bursts and flat stretches in time series using mixture transition distribution (MTD) models. Journal of the American Statistical Association, 91: 1504-1515.

Nhu D. Le, Adrian E. Raftery and R. Douglas Martin (1996). Robust order selection in autoregressive models using robust Bayes factors. Journal of the American Statistical Association, 91: 123-131.

Adrian E. Raftery and Simon Tavare (1994). Estimation and modelling repeated patterns in high-order Markov chains with the mixture transition distribution (MTD) model. Applied Statistics 43: 179-200.

Adrian E. Raftery (1994). Change point and change curve modeling in stochastic processes and spatial statistics. Journal of Applied Statistical Science 1: 403-424. John Haslett and Adrian E. Raftery (1989). Space-time modelling with long-memory dependence: Assessing Ireland's wind power resource (with Discussion). Applied Statistics 38: 1-50.


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