Power of graph-based two-sample tests
Abstract/Contents
- Abstract
- Testing equality of two multivariate distributions is a classical problem for which many non-parametric tests have been proposed over the years. Most of the popular tests are based either on geometric graphs constructed using inter-point distances between the observations (multivariate generalizations of the Wald-Wolfowitz's runs test) or on multivariate data-depth (generalizations of the Mann-Whitney rank test). These tests are known to be asymptotically normal under the null and consistent against fixed alternatives. In this thesis, a general framework for graph-based tests will be introduced that includes all these tests. The asymptotic efficiency of a general graph-based test can be derived using Le Cam's theory of local asymptotic normality, which provides a theoretical basis for comparing the performances of these tests. As a consequence, it will be shown that tests based on geometric graphs such as the Friedman-Rafsky test (1979), the test based on the $K$-nearest neighbor graph (1984), the minimum matching test of Rosenbaum (2005), among others, have zero asymptotic (Pitman) efficiency against $O(N^{-\frac{1}{2}})$ alternatives. On the other hand, the tests based on multivariate depth functions (the Liu-Singh rank sum statistic (1993)), which includes the Tukey depth (1975) and the projection depth (2003), have non-zero asymptotic efficiency. Finally, the limiting normal distribution of tests based on stabilizing random geometric graphs will be derived in the Poissonized setting. This can be used to compute the power of such tests against local alternatives, which validates the various applications of these tests and provides a way to compare between tests with zero Pitman efficiency.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Bhattacharya, Bhaswar B |
|---|---|
| Associated with | Stanford University, Department of Statistics. |
| Primary advisor | Diaconis, Persi |
| Thesis advisor | Diaconis, Persi |
| Thesis advisor | Chatterjee, Sourav |
| Thesis advisor | Friedman, J. H. (Jerome H.) |
| Thesis advisor | Holmes, Susan, 1954- |
| Advisor | Chatterjee, Sourav |
| Advisor | Friedman, J. H. (Jerome H.) |
| Advisor | Holmes, Susan, 1954- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Bhaswar B. Bhattacharya. |
|---|---|
| Note | Submitted to the Department of Statistics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Bhaswar Bikram Bhattacharya
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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