The suppport reduction algorithm for computing nonparametric function estimates in mixture models

Details The suppport reduction algorithm for computing nonparametric function estimates in mixture models The suppport reduction algorithm for computing nonparametric function estimates in mixture models

2004-05-26

arxiv.org/abs/math/0405511v1

Mathematics

Statistics Theory

19 pages. See also http://www.cs.vu.nl/sto/publications/2002-13.ps

Scientific paper

Vertex direction algorithms have been around for a few decades in the experimental design and mixture models literature. We briefly review this type of algorithm and describe a new member of the family: the support reduction algorithm. The support reduction algorithm is applied to the problem of computing nonparametric estimates in two inverse problems: convex density estimation and the Gaussian deconvolution problem. Usually, VD algorithms solve a finite dimensional (version of the) optimization problem of interest. We introduce a method to solve the true infinite dimensional optimization problem.

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