Testing Multiple Profiles for Outlier Detection
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Marjan Rajabi * |
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Abstract: (2884 Views) |
The advent of new technology in recent years has facilitated the production of high dimension data. In these data we need evaluating more than one assumption. Multiple testing can be used for the collection of assumptions that are simultaneously tested and controlled the rate of family wise error that is the most critical issue in such tests. In this report, the authors apply Sidak and Stepwise strategies for controlling family wise error rate in detecting outlier profiles and comparing to each other. Considering our simulation results, the performance of such methods are compared using the parametric bootstrap snd by applying on real data in dataset illustrate the implementation of the proposed methods. |
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Keywords: Multiple Testing, Family Wise Error Rate, Parametric Bootstraps. |
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Full-Text [PDF 220 kb]
(1316 Downloads)
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Type of Study: Applied |
Subject:
Applied Statistics Received: 2019/05/20 | Accepted: 2019/12/2 | Published: 2020/02/20
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