Nonparametric Statistical Inference for Ergodic Processes
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
Cite
@article{arxiv.0804.0510,
title = {Nonparametric Statistical Inference for Ergodic Processes},
author = {Daniil Ryabko and Boris Ryabko},
journal= {arXiv preprint arXiv:0804.0510},
year = {2012}
}
Comments
Conference version in: D. Ryabko, B. Ryabko, On hypotheses testing for ergodic processes, in Proceedgings of Information Theory Workshop, 2008, Porto, Portugal, pp. 281-283