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Prediction and Classification of Non-stationary Categorical Time Series

✍ Scribed by Konstantinos Fokianos; Benjamin Kedem

Publisher
Elsevier Science
Year
1998
Tongue
English
Weight
316 KB
Volume
67
Category
Article
ISSN
0047-259X

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✦ Synopsis

Partial likelihood analysis of a general regression model for the analysis of nonstationary categorical time series is presented, taking into account stochastic time dependent covariates. The model links the probabilities of each category to a covariate process through a vector of time invariant parameters. Under mild regularity conditions, we establish good asymptotic properties of the estimator by appealing to martingale theory. Certain diagnostic tools are presented for checking the adequacy of the fit.

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