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Jiang J, Nguyen T, Rao JS. The E-MS Algorithm: Model Selection with Incomplete Data. J Am Stat Assoc. 2014;110(511):1136-1147doi: 10.1080/01621459.2014.948545.
Jiang, J., Nguyen, T., & Rao, J. S. (2015). The E-MS Algorithm: Model Selection with Incomplete Data. Journal of the American Statistical Association, 110(511), 1136-1147. https://doi.org/10.1080/01621459.2014.948545
Jiang, Jiming, et al. "The E-MS Algorithm: Model Selection with Incomplete Data." Journal of the American Statistical Association vol. 110,511 (2015): 1136-1147. doi: https://doi.org/10.1080/01621459.2014.948545
Jiang J, Nguyen T, Rao JS. The E-MS Algorithm: Model Selection with Incomplete Data. J Am Stat Assoc. 2015 Apr 04;110(511):1136-1147. doi: 10.1080/01621459.2014.948545. Epub 2014 Aug 15. PMID: 26783375; PMCID: PMC4714800.
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J Am Stat Assoc. 2015 Apr 04;110(511):1136-1147. doi: 10.1080/01621459.2014.948545. Epub 2014 Aug 15.

The E-MS Algorithm: Model Selection with Incomplete Data.

Journal of the American Statistical Association

Jiming Jiang, Thuan Nguyen, J Sunil Rao

Affiliations

  1. University of California, Davis, Oregon Health and Science University and University of Miami.

PMID: 26783375 PMCID: PMC4714800 DOI: 10.1080/01621459.2014.948545

Abstract

We propose a procedure associated with the idea of the E-M algorithm for model selection in the presence of missing data. The idea extends the concept of parameters to include both the model and the parameters under the model, and thus allows the model to be part of the E-M iterations. We develop the procedure, known as the E-MS algorithm, under the assumption that the class of candidate models is finite. Some special cases of the procedure are considered, including E-MS with the generalized information criteria (GIC), and E-MS with the adaptive fence (AF; Jiang

Keywords: backcross experiments; conditional sampling; consistency; convergence; missing data mechanism; model selection; regression

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