3ICT SBRAS - Institute of Computational Technologies (Institute of Computational Technologies Siberian Branch of the Russian Academy of Sciences 6 Acad. Lavrentjev avenue 630090 Novosibirsk Russia - Russie)
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.
https://hal.inria.fr/inria-00269249
Contributeur : Daniil Ryabko
<>
Soumis le : samedi 24 mars 2012 - 15:57:03
Dernière modification le : jeudi 11 janvier 2018 - 06:22:13
Document(s) archivé(s) le : lundi 25 juin 2012 - 02:22:04
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. 〈inria-00269249v4〉
