Nonparametric Statistical Inference for Ergodic Processes

Details Nonparametric Statistical Inference for Ergodic Processes Nonparametric Statistical Inference for Ergodic Processes

2008-04-03

arxiv.org/abs/0804.0510v4

IEEE Transactions on Information Theory 56, 3 (2010) 1430-1435

Computer Science

Information Theory

Conference version in: D. Ryabko, B. Ryabko, On hypotheses testing for ergodic processes, in Proceedgings of Information Theor

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Nonparametric Statistical Inference for Ergodic Processes does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Nonparametric Statistical Inference for Ergodic Processes, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Nonparametric Statistical Inference for Ergodic Processes will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-352305