Skip to Main content Skip to Navigation
Journal articles

Nonparametric Statistical Inference for Ergodic Processes

Daniil Ryabko 1, * Boris Ryabko 2, 3
* Corresponding author
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe
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.
Complete list of metadata

Cited literature [26 references]  Display  Hide  Download
Contributor : Daniil Ryabko Connect in order to contact the contributor
Submitted on : Saturday, March 24, 2012 - 3:57:03 PM
Last modification on : Thursday, January 20, 2022 - 4:17:23 PM
Long-term archiving on: : Monday, June 25, 2012 - 2:22:04 AM


Files produced by the author(s)




Daniil Ryabko, Boris Ryabko. Nonparametric Statistical Inference for Ergodic Processes. IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2010, 56 (3), pp.1430-1435. ⟨10.1109/TIT.2009.2039169⟩. ⟨inria-00269249v4⟩



Les métriques sont temporairement indisponibles