Permutation tests for complex data
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Permutation tests for complex data theory, applications, and software by Fortunato Pesarin

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Published by Wiley in Hoboken, N.J .
Written in English

Subjects:

  • Statistical hypothesis testing,
  • Permutations,
  • Multivariate analysis

Book details:

Edition Notes

Includes bibliographical references and index.

StatementFortunato Pesarin, Luigi Salmaso.
ContributionsSalmaso, Luigi.
Classifications
LC ClassificationsQA277 .P374 2010
The Physical Object
Paginationp. cm.
ID Numbers
Open LibraryOL24028881M
ISBN 109780470516416
LC Control Number2010000776

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  Complex multivariate testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. As a result, modern statistics needs permutation testing for complex data with low sample size and many variables, especially in Cited by: Complex multivariate testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. As a result, modern statistics needs permutation testing for complex data with low sample size and many variables, especially in observational studies. Permutation Tests for Complex Data: Theory, Applications and Software Written for researchers and practitioners, this book provides a general overview of permutation tests with a focus on recent theoretical advances within univariate and multivariate complex permutation testing problems. PERMUTATION TESTS FOR COMPLEX DATA. Theory, Applications and Software. Fortunato Pesarin • Luigi Salmaso. University of Padua, Italy. A John Wiley and Sons, Ltd., Publication.

  Nonparametric testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. This book summarizes traditional rank techniques and more recent developments in permutation testing as robust tools for dealing with complex data with low sample size. A permutation test (also called a randomization test, re-randomization test, or an exact test) is a type of statistical significance test in which the distribution of the test statistic under the null hypothesis is obtained by calculating all possible values of the test statistic under all possible rearrangements of the observed data points. 20 Complex ecological data sets in the t distribution which are as large as or larger than the signed value of the reference t statistic, for a test in the right-hand tail (Η1: ρ > 0). In standard statistical tests, the test statistic computed from the data is referred to one of the usual statistical distributions printed in books . The book reviews information on feasibility screening, formulation and packaging development, sensory tests, and more. The authors – noted experts in the field – explore relevant techniques for data analytics and present the guidelines for data interpretation.

To test whether the specific path contributed to the disease end point, we conducted a series of hypothesis tests. The permutation-based hypothesis tests were conducted as follows: 1) draw a large number of data on disease status (e.g., case and control group) without replacement and estimate PSE in each group, and make difference between two. "Complex multivariate testing problems are frequently encountered in many scientific disciplines, such as engineering, medicine and the social sciences. As a result, modern statistics needs permutation testing for complex data with low sample size and many variables, especially in observational studies.". Permutation tests, in particular, will be extended to multivariate and more complex studies, where we deal with several genes and several alleles together. Furthermore, we show simulations under different assumptions on the genetic model and analyse real data sets by simply studying one locus with the permutation test. In contrast, the permutation distribution of our test statistic is discrete, as it is obtained from the finite set of \((n_1+n_2)!\) permutations Or a random subset, in case we want to save computation time. of the observation labels, from a single instance of the data (the \(n_1+n_2\) observations).