PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Nonparametric Statistical Inference for Ergodic Processes
Daniil Ryabko and Boris Ryabko
IEEE Transactions on Information Theory Volume 56, Number 3, pp. 1430-1435, 2010. ISSN 0018-9448

Abstract

In this work a method for statistical analysis of time series is proposed, which is used to obtain solutions to some classical problems of mathematical statistics under the only assumption that the process generating the data is stationary ergodic. Namely, three problems are considered: goodness-of-fit (or identity) testing, process classification, and the change point problem. For each of the problems a test is constructed that is asymptotically accurate for the case when the data is generated by stationary ergodic processes. The tests are based on empirical estimates of distributional distance.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:5969
Deposited By:Daniil Ryabko
Deposited On:08 March 2010