Title :
Nonparametric Statistical Inference for Ergodic Processes
Author :
Ryabko, Daniil ; Ryabko, Boris
Author_Institution :
SequeL, INRIA-Lille Nord Eur., Villeneuve-d´´Ascq, France
fDate :
3/1/2010 12:00:00 AM
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;
Journal_Title :
Information Theory, IEEE Transactions on
DOI :
10.1109/TIT.2009.2039169
