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Communication Dans Un Congrès Année : 2009

Asymptotic Properties of Nonlinear Least Squares Estimates in Stochastic Regression Models Over a Finite Design Space. Application to Self-Tuning Optimisation

Résumé

We present new conditions for the strong consistency and asymptotic normality of the least squares estimator in nonlinear stochastic models when the design variables vary in a finite set. The application to self-tuning optimisation is considered, with a simple adaptive strategy that guarantees simultaneously the convergence to the optimum and the strong consistency and asymptotic normality of the estimates of the model parameters. An illustrative example is presented.
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Dates et versions

hal-00407817 , version 1 (27-07-2009)

Identifiants

  • HAL Id : hal-00407817 , version 1

Citer

Luc Pronzato. Asymptotic Properties of Nonlinear Least Squares Estimates in Stochastic Regression Models Over a Finite Design Space. Application to Self-Tuning Optimisation. 15th IFAC Symposium on System Identification, Jun 2009, Saint Malo, France. pp.156-161. ⟨hal-00407817⟩
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