A Coincidence-Based Test for Uniformity Given Very Sparsely Sampled Discrete Data

• Published in: IEEE transactions on information theory Vol. 54; no. 10; pp. 4750 - 4755
• Main Author: Paninski, L.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.10.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Computer errors; Computer science; Convex bounds; Data analysis; Data processing; Distributed computing; Distribution channels; Engineering profession; Entropy; Exact sciences and technology; hypothesis testing; Information theory; Information, signal and communications theory; minimax; Minimax techniques; Samples; Statistical analysis; Statistical distributions; Telecommunications and information theory; Testing; Upper bound; Variability; Hypothesis test; Uniform distribution; hypothesis testing; Minimax method; A priori estimation; Discrete data; Convex bounds; minimax

How many independent samples N do we need from a distribution p to decide that p is epsiv-distant from uniform in an L 1 sense, Sigma i=1 m | p ( i ) - 1/ m | > epsiv? (Here m is the number of bins on which the distribution is supported, and is assumed known a priori .) Somewhat surprisingly, we...