A fast algorithm for maximum likelihood estimation of mixture proportions using sequential quadratic programming

4 Jun 2018 Youngseok Kim Peter Carbonetto Matthew Stephens Mihai Anitescu

Maximum likelihood estimation of mixture proportions has a long history, and continues to play an important role in modern statistics, including in development of nonparametric empirical Bayes methods. Maximum likelihood of mixture proportions has traditionally been solved using the expectation maximization (EM) algorithm, but recent work by Koenker & Mizera shows that modern convex optimization techniques -- in particular, interior point methods -- are substantially faster and more accurate than EM... (read more)

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