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Overview of Maximum Likelihood Estimation

  • Frank E. HarrellJr.
Part of the Springer Series in Statistics book series (SSS)

Abstract

In ordinary least squares multiple regression, the objective in fitting a model is to find the values of the unknown parameters that minimize the sum of squared errors of prediction. When the response variable is polytomous or is not observed completely, a more general objective to optimize is needed.

Keywords

Maximum Likelihood Estimate Penalty Function Wald Statistic Bootstrap Distribution Observe Information Matrix 
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 Science+Business Media New York 2001

Authors and Affiliations

  • Frank E. HarrellJr.
    • 1
  1. 1.Department of BiostatisticsVanderbilt University School of MedicineNashvilleUSA

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