Weak Convergence Analysis of Asymptotically Optimal Hypothesis Tests
| Autor: | Jayakrishnan Unnikrishnan, Dayu Huang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Weak convergence
020206 networking & telecommunications 0102 computer and information sciences 02 engineering and technology Library and Information Sciences 01 natural sciences Statistical power Computer Science Applications Asymptotically optimal algorithm Goodness of fit 010201 computation theory & mathematics Outlier Statistics 0202 electrical engineering electronic engineering information engineering Test statistic Convergence tests Information Systems Statistical hypothesis testing Mathematics |
| Zdroj: | IEEE Transactions on Information Theory. 62:4285-4299 |
| ISSN: | 1557-9654 0018-9448 |
| DOI: | 10.1109/tit.2016.2563439 |
| Popis: | In recent years, solutions to various hypothesis testing problems in the asymptotic setting have been proposed using the results from large deviation theory. Such tests are optimal in terms of appropriately defined error exponents. For the practitioner, however, error probabilities in the finite sample size setting are more important. In this paper, we show how results on weak convergence of the test statistic can be used to obtain better approximations for the error probabilities in the finite sample size setting. While this technique is popular among statisticians for common tests, we demonstrate its applicability for several recently proposed asymptotically optimal tests, including tests for robust goodness of fit, homogeneity tests, outlier hypothesis testing, and graphical model estimation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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