Permutation Tests for Complex Data

By Fortunato Pesarin, Luigi Salmaso. 2010

629 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/46e4d977ca7e95d2a23dfb04604575484e21234b

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Citations in the corpus

The permutation testing approach: a review (2010, methodology): “In order to be suitable for test combination (Pesarin, 2001; Pesarin and Salmaso, 2010), all combining functions must satisfy at least the following reasonable properties: PC.1.”
Permutation inference for the general linear model (2014, background): “Under the assumption of independent and symmetric errors, the problem is solved by means of random sign ﬂ ipping (Pesarin, 1995), using the well known Welch's v statistic, a particular case of the statistic G shown in Eq.”
Asymptotic permutation tests in general factorial designs (2015, neutral): “It is well known that permutation tests are finitely exact under the assumption of exchangeability of the data; see for example Pesarin (2001), Good (2005), Edgington and Onghena (2007), Mielke and Berry (2007) or Pesarin and Salmaso (2010) and the references therein for a multitude of examples.”
Interval-wise testing for functional data (2017, positive): “…the number of permutations of the data to be explored to evaluate the p -values is extremely high in at least some of the studied cases, a Conditional Monte Carlo (CMC) algorithm is applied to estimate the p -values of the tests of H I 0 against H I 1 for any interval I (Pesarin and Salmaso 2010).”
Randomization tests for single-case experiments: State of the art, state of the science, and state of the application (2014, methodology): “…1972a; Onghena & Edgington, 2005), the multiplicative method (Edgington, 1972a; Pesarin, 2001; Rosenthal, 1978), the normal curve method for situations with four or more p values (Edgington, 1972b), and the iterative procedure for combining p values (Pesarin, 2001; Pesarin & Salmaso, 2010).”
Permutation-based simultaneous confidence bounds for the false discovery proportion (2018, background): “Sometimes other groups of transformations can be used, such as rotations (Langsrud, 2005; Solari et al., 2014) and multiplication of part of the data by − 1 (Pesarin and Salmaso (2010), pp. 54 and 168).”
Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies (2011, positive): “To overcome these problems and to give a measure of the statistical significance of the diagnostic statistics (P-value), a permutation test was introduced (Lindgren et al. 1996; Golland et al. 2005; Mielke and Berry 2007; Pesarin and Salmaso 2010).”
nparcomp: An R Software Package for Nonparametric Multiple Comparisons and Simultaneous Confidence Intervals (2015, neutral): “Therefore caution should be paid when drawing inferences (see Pesarin and Salmaso (2010), p. 10ff.).”
Minimax optimality of permutation tests (2020, methodology): “This section briefly overviews the permutation procedure and its well-known theoretical properties, referring readers to Lehmann and Romano (2006); Pesarin and Salmaso (2010) for more details.”