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math.ST2022
Computing Euclidean distance and maximum likelihood retraction maps for constrained optimization
Alexander Heaton, Matthias Himmelmann
Riemannian optimization uses local methods to solve optimization problems whose constraint set is a smooth manifold. A linear step along some descent direction usually leaves the c…
math.ST2020★ 11 cited
Logarithmic Voronoi cells
Yulia Alexandr, Alexander Heaton
We study Voronoi cells in the statistical setting by considering preimages of the maximum likelihood estimator that tessellate an open probability simplex. In general, logarithmic…
math.ST2020★ 3 cited
Exact Solutions in Log-Concave Maximum Likelihood Estimation
Alexandros Grosdos, Alexander Heaton, Kaie Kubjas +3
We study probability density functions that are log-concave. Despite the space of all such densities being infinite-dimensional, the maximum likelihood estimate is the exponential…