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Likelihood Transformations and Artificial Mixtures

In: Statistical Modeling for Biological Systems

Author

Listed:
  • Alex Tsodikov

    (School of Public Health, Department of Biostatistics, University of Michigan)

  • Lyrica Xiaohong Liu

    (Amgen)

  • Carol Tseng

    (H2O Clinical, LLC)

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.

Suggested Citation

  • Alex Tsodikov & Lyrica Xiaohong Liu & Carol Tseng, 2020. "Likelihood Transformations and Artificial Mixtures," Springer Books, in: Anthony Almudevar & David Oakes & Jack Hall (ed.), Statistical Modeling for Biological Systems, pages 191-209, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-34675-1_11
    DOI: 10.1007/978-3-030-34675-1_11
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