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Nonparametric Statistical Inference for Ergodic Processes
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.
Cited literature [26 references]
https://hal.inria.fr/inria-00269249
Contributor : Daniil Ryabko <>
Submitted on : Saturday, March 24, 2012 - 3:57:03 PM
Last modification on : Thursday, February 21, 2019 - 10:52:49 AM
Document(s) archivé(s) le : Monday, June 25, 2012 - 2:22:04 AM
Contributor : Daniil Ryabko <>
Submitted on : Saturday, March 24, 2012 - 3:57:03 PM
Last modification on : Thursday, February 21, 2019 - 10:52:49 AM
Document(s) archivé(s) le : Monday, June 25, 2012 - 2:22:04 AM
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