Nonlinear experimental design based on the distribution of estimators

DOI10.1016/0378-3758(92)90007-FzbMath0772.62042OpenAlexW2037883727MaRDI QIDQ1205477

Luc Pronzato, Andrej Pázman

Publication date: 1 April 1993

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0378-3758(92)90007-f

zbMATH Keywords

approximationmaximum likelihood estimatormean square errorpenalty functionstochastic gradientnormal errorsconstrained least-squares estimatesdensity of estimatesoptimal designs with fixed size

Mathematics Subject Classification ID

Optimal statistical designs (62K05) General nonlinear regression (62J02) Stochastic approximation (62L20) Approximations to statistical distributions (nonasymptotic) (62E17)

Related Items (8)

Optimal designs based on exact confidence regions for parameter estimation of a nonlinear regression model ⋮ A Dirac-function method for densities of nonlinear statistics and for marginal densities in nonlinear regression ⋮ Distribution of the multivariate nonlinear LS estimator under an uncertain input ⋮ Optimal experimental design and some related control problems ⋮ Optimum design accounting for the global nonlinear behavior of the model ⋮ Unnamed Item ⋮ Designs in nonlinear regression by stochastic minimization of functionals of the mean square error matrix ⋮ Constrained design strategies for improving normal approximations in nonlinear regression problems

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• Recent Advances in Nonlinear Experimental Design
• Stochastic approximation methods for identification and control--A survey

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