Strong consistency and asymptotic normality of least squares estimates in stochastic regression models are established under certain weak assumptions on the stochastic regressors and errors. We discuss applications of these results to interval estimation of the regression parameters and to recursive on-line identification and control schemes for linear dynamic systems.
Publié le : 1982-03-14
Classification:
Stochastic regressors,
least squares,
system identification,
adaptive control,
dynamic models,
strong consistency,
asymptotic normality,
martingales,
62J05,
62M10,
60F15,
60G45,
93B30,
93C40
@article{1176345697,
author = {Lai, Tze Leung and Wei, Ching Zong},
title = {Least Squares Estimates in Stochastic Regression Models with Applications to Identification and Control of Dynamic Systems},
journal = {Ann. Statist.},
volume = {10},
number = {1},
year = {1982},
pages = { 154-166},
language = {en},
url = {http://dml.mathdoc.fr/item/1176345697}
}
Lai, Tze Leung; Wei, Ching Zong. Least Squares Estimates in Stochastic Regression Models with Applications to Identification and Control of Dynamic Systems. Ann. Statist., Tome 10 (1982) no. 1, pp. 154-166. http://gdmltest.u-ga.fr/item/1176345697/