Locating the minimum of a function when the errors of observation have unknown density

DOI10.1007/BF02481053zbMath0513.62083OpenAlexW2085132078MaRDI QIDQ1050733

Thomas E. Obremski

Publication date: 1982

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02481053

zbMATH Keywords

asymptotic normalityefficient estimationKiefer-Wolfowitz procedurelocation of minima of functionsunknown observation error densities

Mathematics Subject Classification ID

Stochastic approximation (62L20)

Cites Work

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• Transformation of observations in stochastic approximation
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• On Asymptotic Normality in Stochastic Approximation
• Stochastic Estimation of the Maximum of a Regression Function
• A Stochastic Approximation Method
• Multidimensional Stochastic Approximation Methods

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