Limits to classification and regression estimation from ergodic processes

Nobel, Andrew B.

Ann. Statist., Tome 27 (1999) no. 4, p. 262-273 / Harvested from Project Euclid

Résumé

We answer two open questions concerning the existence of universal schemes for classification and regression estimation from stationary ergodic processes. It is shown that no measurable procedure can produce weakly consistent regression estimates from every bivariate stationary ergodic process, even if the covariate and response variables are restricted to take values in the unit interval. It is further shown that no measurable procedure can produce weakly consistent classification rules from every bivariate stationary ergodic process for which the response variable is binary valued. The results of the paper are derived via reduction arguments and are based in part on recent work concerning density estimaton from ergodic processes.

Détail

Publié le : 1999-03-14
Classification:  Classification,  regression,  ergodic processes,  counterexamples,  reduction arguments,  62G07,  60G10,  62M99