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The AI&M Procedure for Learning from Incomplete Data
[article]
2012
arXiv
pre-print
We investigate methods for parameter learning from incomplete data that is not missing at random. Likelihood-based methods then require the optimization of a profile likelihood that takes all possible missingness mechanisms into account. Optimzing this profile likelihood poses two main difficulties: multiple (local) maxima, and its very high-dimensional parameter space. In this paper a new method is presented for optimizing the profile likelihood that addresses the second difficulty: in the
arXiv:1206.6830v1
fatcat:ubrog5cphnfubefgfnpjrop7nq