Skip to main content
Article
Combining Functions and the Closure Principle for Performing Follow-Up Tests in Functional Analysis of Variance
Computational Statistics & Data Analysis (2013)
  • Olga A. Vsevolozhskaya
  • Mark C. Greenwood
  • G. J. Bellante
  • S. L. Powell
  • R. L. Lawrence
  • K. S. Repasky
Abstract

Functional analysis of variance involves testing for differences in functional means across kk groups in nn 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
Publication Date
November, 2013
Publisher Statement

Copyright © 2013 Elsevier B.V. All rights reserved.

This manuscript version is made available under the CC‐BY‐NC‐ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Citation Information
Olga A. Vsevolozhskaya, Mark C. Greenwood, G. J. Bellante, S. L. Powell, et al.. "Combining Functions and the Closure Principle for Performing Follow-Up Tests in Functional Analysis of Variance" Computational Statistics & Data Analysis Vol. 67 (2013)
Available at: http://works.bepress.com/vsevolozhskaya/7/