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Activity Number: 582 - Statistical Methods for Functional Data
Type: Contributed
Date/Time: Wednesday, August 2, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #324800
Title: Estimation of the Likelihood for Context Set Models
Author(s): Zsolt Talata*
Companies: University of Kansas
Keywords: context set ; context tree ; Markov chain ; time series ; statistical estimation ; double mixture

Finitely-valued stationary time series are described by the collection of the conditional probabilities of the possible values given the infinite pasts. The concept of context is extended to be an arbitrary part - not necessarily a continuous ending - of the past that determines the transition probability. The context set model of the time series consists of the collection of all contexts and the corresponding transition probabilities. The likelihood is estimated from a sample using a double mixture over the possible models and their parameters. An optimality of the estimator is proved and an algorithm is shown to calculate the estimator in reasonable time despite the very large number of possible models.

Authors who are presenting talks have a * after their name.

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