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Statistical estimation from an optimization viewpoint

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  • Roger Wets

Abstract

Statistics and Optimization have been closely linked from the very outset. The search fora “best” estimator (least squares, maximum likelihood, etc.) certainly relies on optimizationtools. On the other hand, Statistics has often provided the motivation for the development ofalgorithmic procedures for certain classes of optimization problems. However, it is onlyrelatively recently, more specifically in connection with the development of an approximationand sampling theory for stochastic programming problems, that the full connectionhas come to light. This in turn suggests a more comprehensive approach to the formulationof statistical estimation questions. This expository paper reviews some of the features ofthis approach. Copyright Kluwer Academic Publishers 1999

Suggested Citation

  • Roger Wets, 1999. "Statistical estimation from an optimization viewpoint," Annals of Operations Research, Springer, vol. 85(0), pages 79-101, January.
    • Handle: RePEc:spr:annopr:v:85:y:1999:i:0:p:79-101:10.1023/a:1018934214007
    DOI: 10.1023/A:1018934214007
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    Cited by:

    1. Pardalos, Panos M. & Sackellares, J. Chris & Iasemidis, Leonidas D. & Yatsenko, Vitality & Yang, Mark C. K. & Shiau, Deng-Shan & Chaovalitwongse, Wanpracha, 2003. "Statistical information approaches for the modelling of the epileptic brain," Computational Statistics & Data Analysis, Elsevier, vol. 43(1), pages 79-108, May.
    2. Jong-Shi Pang & Che-Lin Su & Yu-Ching Lee, 2015. "A Constructive Approach to Estimating Pure Characteristics Demand Models with Pricing," Operations Research, INFORMS, vol. 63(3), pages 639-659, June.
    3. Alexander Weissman, 2013. "Optimizing information using the EM algorithm in item response theory," Annals of Operations Research, Springer, vol. 206(1), pages 627-646, July.
    4. Alexander Weissman, 2013. "Global Convergence of the EM Algorithm for Unconstrained Latent Variable Models with Categorical Indicators," Psychometrika, Springer;The Psychometric Society, vol. 78(1), pages 134-153, January.

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