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Likelihood Transformations and Artificial Mixtures

  • Alex TsodikovEmail author
  • Lyrica Xiaohong Liu
  • Carol Tseng
Chapter
  • 41 Downloads

Abstract

In this paper we consider the generalized self-consistency approach to maximum likelihood estimation (MLE). The idea is to represent a given likelihood as a marginal one based on artificial missing data. The computational advantage is sought in the likelihood simplification at the complete-data level. Semiparametric survival models and models for categorical data are used as an example. Justifications for the approach are outlined when the model at the complete-data level is not a legitimate probability model or if it does not exist at all.

Notes

Acknowledgement

This research is supported by National Cancer Institute grant U01 CA97414 (CISNET).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Alex Tsodikov
    • 1
    Email author
  • Lyrica Xiaohong Liu
    • 2
  • Carol Tseng
    • 3
  1. 1.University of MichiganSchool of Public Health, Department of BiostatisticsAnn ArborUSA
  2. 2.AmgenSouth San FranciscoUSA
  3. 3.H2O Clinical, LLCHunt ValleyUSA

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