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Least squares estimates in stochastic regression models with applications to identification and control of dynamic systems. (English) Zbl 0649.62060
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.

MSC:
62J05 Linear regression; mixed models
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
93E12 Identification in stochastic control theory
93B30 System identification
93C40 Adaptive control/observation systems
60F15 Strong limit theorems
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