publication . Other literature type . Preprint . 2014

On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives

Aaditya Ramdas; Sashank J. Reddi; Poczos, Barnabas; Singh, Aarti; Wasserman, Larry;
Open Access
  • Published: 01 Jan 2014
  • Publisher: Carnegie Mellon University
Nonparametric two sample testing deals with the question of consistently deciding if two distributions are different, given samples from both, without making any parametric assumptions about the form of the distributions. The current literature is split into two kinds of tests - those which are consistent without any assumptions about how the distributions may differ (\textit{general} alternatives), and those which are designed to specifically test easier alternatives, like a difference in means (\textit{mean-shift} alternatives). The main contribution of this paper is to explicitly characterize the power of a popular nonparametric two sample test, designed for ...
free text keywords: 170203 Knowledge Representation and Machine Learning, FOS: Psychology, Mathematics - Statistics Theory, Computer Science - Artificial Intelligence, Computer Science - Information Theory, Computer Science - Learning, Statistics - Machine Learning
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