Combining functions and the closure principle for performing follow-up tests in functional analysis of variance

O.A. Vsevolozhskaya, M.C. Greenwood, G.J. Bellante, S.L. Powell, R.L. Lawrence and K.S. Repasky

Computational Statistics & Data Analysis, 2013, vol. 67, issue C, 175-184

Abstract: Functional analysis of variance involves testing for differences in functional means across k groups in n functional responses. If a significant overall difference in the mean curves is detected, one may want to identify the location of these differences. Cox and Lee (2008) proposed performing a point-wise test and applying the Westfall–Young multiple comparison correction. We propose an alternative procedure for identifying regions of significant difference in the functional domain. Our procedure is based on a region-wise test and application of a combining function along with the closure multiplicity adjustment principle. We give an explicit formulation of how to implement our method and show that it performs well in a simulation study. The use of the new method is illustrated with an analysis of spectral responses related to vegetation changes from a CO2 release experiment.

Keywords: Functional data analysis; Multiple comparison procedure; Permutation method; Distance-based method (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:67:y:2013:i:c:p:175-184

DOI: 10.1016/j.csda.2013.05.005

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