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Innovations to Increase the Power of State-of-the Art Graph-Theoretic Two Sample Statistical Tests.
| 题名： | Innovations to Increase the Power of State-of-the Art Graph-Theoretic Two Sample Statistical Tests. |
| 作者： | Wallace, M. J. |
| 关键词： | Covariance, Permutations, Statistical distributions, Algorithms, Normal distribution, Simulations, Statistical algorithms, Databases, Network science, Probability, Random variables, Statistical analysis, Statistics, Data set, Digital data, Probability distributions, United states naval academy, Data science, Information science, Statistical tests |
| 摘要： | One of the classic problems in statistics is to determine whether a group of observations can be characterized as statistically different fromsome other group. In the case of the well-known two-sample t-test, observations are univariate (1-dimensional) and underlying probabilitydistributions are normal (or approximately normal). However, in real-world problems, the number of covariates may be very large and theremay be little known about underlying distributions. Finding powerful tests for group differences in this general multivariate case presentschallenges, and this difficult case has attracted recent research interest. |
| 报告类型： | 科技报告 |
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