2026-09-20T13:58:07Zhttps://digital.lib.washington.edu/server/oai/request

oai:digital.lib.washington.edu:1773/481182026-02-15T20:44:01Zcom_1773_4888col_1773_4971

Han, Fang Shi, Hongjian 2021-10-29T16:24:41Z 2021-10-29T16:24:41Z 2021-10-29 2021-10-29 2021-10-29 2021 Shi_washington_0250E_23452.pdf http://hdl.handle.net/1773/48118 Thesis (Ph.D.)--University of Washington, 2021 Testing independence is a fundamental statistical problem that has received much attention in literature. In this dissertation, we consider testing independence under two different settings. The first is testing mutual independence of many covariates, and the second is testing independence of two random vectors. For both settings, we propose, for the first time, distribution-free and consistent tests of independence via marginal or multivariate ranks. Moreover, we establish the optimal efficiency in the statistical sense of both tests. In addition, we also investigate the power of a simple consistent rank correlation coefficient recently proposed by Chatterjee (2021+) against local alternatives. Our results show that Chatterjee's coefficient is unfortunately statistically inefficient. application/pdf en_US none Statistics Statistics Distribution-Free Consistent Tests of Independence via Marginal and Multivariate Ranks Thesis Open Access open.access