Nonparametric inference for ergodic, stationary time series
| dc.creator | Morvai, G. | |
| dc.creator | Yakowitz, S. | |
| dc.creator | Gyorfi, L. | |
| dc.date | 2007-11-02 | |
| dc.date.accessioned | 2026-07-07T09:45:12Z | |
| dc.date.available | 2026-07-07T09:45:12Z | |
| dc.description | The setting is a stationary, ergodic time series. The challenge is to construct a sequence of functions, each based on only finite segments of the past, which together provide a strongly consistent estimator for the conditional probability of the next observation, given the infinite past. Ornstein gave such a construction for the case that the values are from a finite set, and recently Algoet extended the scheme to time series with coordinates in a Polish space. The present study relates a different solution to the challenge. The algorithm is simple and its verification is fairly transparent. Some extensions to regression, pattern recognition, and on-line forecasting are mentioned. | |
| dc.identifier | https://arxiv.org/abs/0711.0367 | |
| dc.identifier | http://arxiv.org/abs/0711.0367 | |
| dc.identifier | Ann. Statist. 24 (1996), no. 1, 370--379 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/163122 | |
| dc.subject | Probability | |
| dc.subject | Information Theory | |
| dc.title | Nonparametric inference for ergodic, stationary time series | |
| dc.type | text |