Nonparametric Statistical Inference for Ergodic Processes

Author

Ryabko, Daniil ; Ryabko, Boris

Author_Institution

SequeL, INRIA-Lille Nord Eur., Villeneuve-d´´Ascq, France

Volume

56

Issue

3

fYear

2010

fDate

3/1/2010 12:00:00 AM

Firstpage

1430

Lastpage

1435

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.

Keywords

nonparametric statistics; statistical analysis; time series; change point problem; goodness-of-fit testing; nonparametric statistical inference; process classification; stationary ergodic processes; time series statistical analysis; Europe; Informatics; Information theory; Materials science and technology; Pattern recognition; Statistical analysis; Statistical learning; Telecommunication computing; Testing; Time series analysis; Change point problem; goodness-of-fit test; nonparametric hypothesis testing; process classification; stationary ergodic processes;

fLanguage

English

Journal_Title

Information Theory, IEEE Transactions on

Publisher

ieee

ISSN

0018-9448

Type

jour

DOI

10.1109/TIT.2009.2039169

Filename

5429133