Strongly consistent nonparametric forecasting and regression for stationary ergodic sequences
| dc.creator | Yakowitz, S. | |
| dc.creator | Gyorfi, L. | |
| dc.creator | Kieffer, J. | |
| dc.creator | Morvai, G. | |
| dc.date | 2007-12-16 | |
| dc.date.accessioned | 2026-07-07T09:45:13Z | |
| dc.date.available | 2026-07-07T09:45:13Z | |
| dc.description | Let $\{(X_i,Y_i)\}$ be a stationary ergodic time series with $(X,Y)$ values in the product space $\R^d\bigotimes \R .$ This study offers what is believed to be the first strongly consistent (with respect to pointwise, least-squares, and uniform distance) algorithm for inferring $m(x)=E[Y_0|X_0=x]$ under the presumption that $m(x)$ is uniformly Lipschitz continuous. Auto-regression, or forecasting, is an important special case, and as such our work extends the literature of nonparametric, nonlinear forecasting by circumventing customary mixing assumptions. The work is motivated by a time series model in stochastic finance and by perspectives of its contribution to the issues of universal time series estimation. | |
| dc.identifier | https://arxiv.org/abs/0712.2592 | |
| dc.identifier | http://arxiv.org/abs/0712.2592 | |
| dc.identifier | J. Multivariate Anal. 71 (1999), no. 1, 24--41 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/163127 | |
| dc.subject | Probability | |
| dc.subject | Information Theory | |
| dc.title | Strongly consistent nonparametric forecasting and regression for stationary ergodic sequences | |
| dc.type | text |