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Erschienen in:
Statistics, Statisticians, and the Internet of Things Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

8. Hypothesis Testing for High-Dimensional Data

verfasst von : Wei Biao Wu, Zhipeng Lou, Yuefeng Han

Erschienen in: Handbook of Big Data Analytics

Verlag: Springer International Publishing

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Abstract

We present a systematic theory for tests for means of high-dimensional data. Our testing procedure is based on an invariance principle which provides distributional approximations of functionals of non-Gaussian vectors by those of Gaussian ones. Differently from the widely used Bonferroni approach, our procedure is dependence-adjusted and has an asymptotically correct size and power. To obtain cutoff values of our test, we propose a half-sampling method which avoids estimating the underlying covariance matrix of the random vectors. The latter method is shown via extensive simulations to have an excellent performance.

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Vorheriges Kapitel Variational Bayes for Hierarchical Mixture Models
Nächstes Kapitel High-Dimensional Classification
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Metadaten
Titel
Hypothesis Testing for High-Dimensional Data
verfasst von
Wei Biao Wu
Zhipeng Lou
Yuefeng Han
Copyright-Jahr
2018
Buch
Handbook of Big Data Analytics

Print ISBN: 978-3-319-18283-4

Electronic ISBN: 978-3-319-18284-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-18284-1_8

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