作者：Kunisky, Dmitriy
作者单位：Johns Hopkins University
摘要：We study when low coordinate degree functions (LCDF)-linear combinations of functions depending on small subsets of entries of a vector-can hypothesis test between high-dimensional probability measures. These functions are a generalization, proposed in Hopkins' 2018 thesis but seldom studied since, of low degree polynomials (LDP), a class widely used in recent literature as a proxy for all efficient algorithms for tasks in statistics and optimization. Instead of the orthogonal polynomial decom...
作者：Stoepker, Ivo v.; Castro, Rui m.; Arias-castro, Ery
作者单位：Eindhoven University of Technology; University of California System; University of California San Diego; University of California System; University of California San Diego
摘要：Detecting anomalies in large sets of observations is crucial in various applications, such as epidemiological studies, gene expression studies, and systems monitoring. We consider settings where the units of interest result in multiple independent observations from potentially distinct referentials. Scan statistics and related methods are commonly used in such settings, but rely on stringent modeling assumptions for proper calibration. We instead propose a rank-based variant of the higher crit...
