Pointwise testing with functional data using the Westfall–Young randomization method

By Dennis D. Cox, Jong Soo Lee. 2008

67 citations recorded by the source corpus.

We consider hypothesis testing with smooth functional data by performing pointwise tests and applying a multiple comparisons procedure. Methods based on general inequalities, such as Bonferroni's method, do not perform well because of the high correlation between observations at nearby points. We consider the multiple comparison procedure proposed by Westfall & Young (1993) and show that it approximates a multiple comparison correction for a continuum of comparisons as the grid for pointwise comparisons becomes finer. Simulations and an application verify that this result applies in practical settings. Copyright 2008, Oxford University Press.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c9cac127d37c14d5e5c0cf5fe3d30107602aa6ba

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Citations in the corpus

Interval-wise testing for functional data (2017, methodology): “Cox and Lee (2008) propose to apply the Westfall and Young adjustment (Westfall and Young 1993) to point-wise p-values. On the same line, Vsevolozhskaya et al. (2015) propose to adjust point-wise p-values by applying the closed testing procedure (Marcus, Peritz, and Gabriel 1976).”
Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings (2015, methodology): “In any case, tests for forecast dominance based on extremal scoring functions, perhaps in concert with the bootstrap or the Westfall–Young method (Westfall and Young, 1993; Cox and Lee, 2008), deserve further investigation.”
The permutation testing approach: a review (2010, background): “…exist in many other research fields, including clinical trials (Agresti and Klingenberg, 2005; Tang et al., 2009), functional data analysis (Cox and Lee, 2008), spatial statistics (Downer, 2002), principal component analysis (Dray, 2008), shape analysis (Brombin and Salmaso, 2009), gene expression…”