作者：Jiang, Zhenyu; Ling, Nengxiang; Lu, Zudi; Tjostheim, Dag; Zhang, Qiang
作者单位：University of Southampton; Hefei University of Technology; University of Bergen; Beijing University of Chemical Technology
摘要：Bandwidth choice is crucial in spatial kernel estimation in exploring non-Gaussian complex spatial data. The paper investigates the choice of adaptive and non-adaptive bandwidths for density estimation given data on a spatial lattice. An adaptive bandwidth depends on local data and hence adaptively conforms with local features of the spatial data. We propose a spatial cross-validation (SCV) choice of a global bandwidth. This is done first with a pilot density involved in the expression for the...
作者：Prasad, Adarsh; Suggala, Arun Sai; Balakrishnan, Sivaraman; Ravikumar, Pradeep
作者单位：Carnegie Mellon University
摘要：We provide a new computationally efficient class of estimators for risk minimization. We show that these estimators are robust for general statistical models, under varied robustness settings, including in the classical Huber epsilon-contamination model, and in heavy-tailed settings. Our workhorse is a novel robust variant of gradient descent, and we provide conditions under which our gradient descent variant provides accurate estimators in a general convex risk minimization problem. We provid...
