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Estimating Equations and Maximum Likelihood

  • Robert W. Keener
Chapter
Part of the Springer Texts in Statistics book series (STS)

Abstract

Many estimators in statistics are specified implicitly as solutions to equations or as values maximizing some function. In this chapter we study why these methods work and learn ways to approximate distributions. Although we focus on methods for i.i.d. observations, many of the ideas can be extended. Results for stationary time series are sketched in Section 9.9.

Keywords

Random Vector Central Limit Theorem Initial Guess Maximum Likelihood Estimator Random Function 
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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Copyright information

© Springer New York 2009

Authors and Affiliations

  • Robert W. Keener
    • 1
  1. 1.Department of StatisticsUniversity of MichiganAnn ArborUSA

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