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Parameter Estimation and Asymptotics

  • M. M. Rao
Part of the Mathematics and Its Applications book series (MAIA, volume 508)

Abstract

In stochastic modeling, probability distributions often involve unknown parameters, and this chapter deals with some principles of estimation of such parameters. That involves various classes of loss functions, and the study concentrates on certain desirable properties of estimators which are (known) functions of random variables. These include a detailed mathematical analysis of Bayes and maximum likelihood estimation as well as (nonlinear) prediction. Also treated are the asymptotics of the methodology to be explored for particular types of processes later on, and a relatively short account of sequential estimation together with some important complements.

Keywords

Loss Function Unbiased Estimator Orlicz Space Sequential Estimation Differentiable Norm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Bibliographical notes

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Copyright information

© Springer Science+Business Media Dordrecht 2000

Authors and Affiliations

  • M. M. Rao
    • 1
  1. 1.University of CaliforniaRiversideUSA

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