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.
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Acknowledgement
This research is supported by National Cancer Institute grant U01 CA97414 (CISNET).
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Tsodikov, A., Liu, L.X., Tseng, C. (2020). Likelihood Transformations and Artificial Mixtures. In: Almudevar, A., Oakes, D., Hall, J. (eds) Statistical Modeling for Biological Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-34675-1_11
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DOI: https://doi.org/10.1007/978-3-030-34675-1_11
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