Likelihood Transformations and Artificial Mixtures

Alex Tsodikov (), Lyrica Xiaohong Liu () and Carol Tseng ()
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Alex Tsodikov: School of Public Health, Department of Biostatistics, University of Michigan
Lyrica Xiaohong Liu: Amgen
Carol Tseng: H2O Clinical, LLC

A chapter in Statistical Modeling for Biological Systems, 2020, pp 191-209 from Springer

Abstract: 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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-34675-1_11

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DOI: 10.1007/978-3-030-34675-1_11

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