Permutation Tests for Linear Models

By Marti J. Anderson, John Robinson. 2001

572 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/07e3035dbdf48221848c3ff39a114d4afa270433

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Citations in the corpus

Permutation tests for multi-factorial analysis of variance (2003, methodology): “It was found that permutation of residuals under a reduced model (Freedman and Lane, 1983) performed the best in the widest set of circumstances (Anderson and Legendre, 1999) and also comes the closest to the conceptually exact test (Anderson and Robinson, 2001).”
Robust Permutation Tests For Correlation And Regression Coefficients (2017, methodology): “A commonly used sub-family of the regression models specified by assumptions (1)-(3) is heteroskedastic models, which assume that the conditional”
Some solutions to the multivariate Behrens–Fisher problem for dissimilarity‐based analyses (2017, background): “…balanced designs) in other contexts where conditioning on ancillary quantities would be desirable; e.g., for tests of partial regression coefficients in multiple regression models (Anderson & Robinson 2001) or for tests of individual factors in multi-way ANOVA designs (Anderson & ter Braak 2003).”
Covariate-adjusted Fisher randomization tests for the average treatment effect (2020, methodology): “Freedman and Lane (1983) and Anderson and Robinson (2001) sketched proofs for the reference distributions of the statistics studentized by the classic standard errors.”
Experimental Evidence from Causal Criteria Analysis for the Effects of Common Carp Cyprinus carpio on Freshwater Ecosystems: A Global Perspective (2015, methodology): “…et al., 2008) following normalization of the data, using a Euclidean distance and 9999 permutations of the residuals under a reduced model (Anderson and Robinson, 2001), and with statistical effects (including a posteriori pair-wise comparisons, in case of significance) evaluated at a D…”
Distance‐Based Tests for Homogeneity of Multivariate Dispersions (2006, background): “For the latter, permutation of the least-squares (LS) residuals, rij = (xij − ci) is appropriate in this context (Anderson and Robinson, 2001).”
Sensitivity of MRQAP Tests to Collinearity and Autocorrelation Conditions (2007, methodology): “We present a new permutation method (called “double semipartialing”, or DSP) that complements the family of extant approaches to MRQAP tests.”
Permutation tests for univariate or multivariate analysis of variance and regression (2001, methodology): “Namely, permutation of residuals under the reduced model is generally to be preferred over permutation of residuals under the full model, as it comes the closest to a conceptually exact test (Anderson and Robinson 2001).”
Minimax optimality of permutation tests (2020, background): “…to a wide range of statistical tasks including testing independence, two-sample testing, change point detection, clustering, classification, principal component analysis (see Anderson and Robinson, 2001; Kirch and Steinebach, 2006; Park et al., 2009; Ojala and Garriga, 2010; Zhou et al., 2018).”
A Statistical Significance Test for Necessary Condition Analysis (2020, methodology): “Since the availability of fast computers, permutation tests have been developed for correlation and regression (Anderson & Robinson, 2001; DiCiccio & Romano, 2017), ANOVA (Anderson, 2001), the general linear model (Winkler, Ridgway, Webster, Smith, & Nichols, 2014), and qualitative comparative…”