Инд. авторы: Ryabko D.B., Ryabko B.Y.
Заглавие: Nonparametric Statistical Inference for Ergodic Processes
Библ. ссылка: Ryabko D.B., Ryabko B.Y. Nonparametric Statistical Inference for Ergodic Processes // IEEE Transactions on Information Theory. - 2010. - Vol.56. - Iss. 3. - P.1430-1435. - ISSN 0018-9448. - EISSN 1557-9654.
Внешние системы: DOI: 10.1109/TIT.2009.2039169; РИНЦ: 15333730; SCOPUS: 2-s2.0-77949528765; WoS: 000275561000040;
Реферат: eng: 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.
Издано: 2010
Физ. характеристика: с.1430-1435