Statistics Seminar: Jiming Jiang (University of California, Davis) -- The E-MS Algorithm: Model Selection with Incomplete Data

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. Some special cases of the E-MS are considered, including
the Markov-chain model for backcross experiments, and the linear
regression model. We prove convergence of the E-MS algorithm. We
address the issue regarding the missing data mechanisms. Furthermore,
we carry out simulation studies on the finite-sample performance of
the E-MS with comparisons to other procedures. A real data example is
considered.

This work is joint with Thuan Nguyen, Oregon Health & Science University,
USA and J. Sunil Rao, University of Miami, USA.