Other Applications

Abstract

In this chapter three additional applications of smoothness priors time series modeling are addressed which for a variety of reasons, were not included in other chapters. The first application is a study of the modeling of a very large data set, (500,000 observations), with missing data and outliers in a complex stochastic trend and regression on covariates modeling (Kitagawa and Matsumoto 1996). The objective of the analysis is to decompose the data into its component parts. The second application is a Markov state classification problem in which each observed state corresponds to a different time series process and the states are switched at random times. An illustrative analysis is done on simulated data. The third application involves an extension of the smoothness priors long AR model for spectral estimation in scalar stationary time series, (discussed in Chapter 4), to the multivariate case.