Processing of Prior-Information in Statistics by Projections on Convex Cones
We investigate a Random-Search-Algorithm for finding the projection on a closed convex cone in R P with respect to a norm defined by any positive definite matrix. It is shown that this algorithm converges almost surely. The power of the algorithm is demonstrated by examples from statistics in which processing of prior information may be formulated as projections of parametervectors on polyhedral cones.
Key wordsRandom search projection on a convex cone optimization in statistics statistical estimation under prior information.
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