Abstract Details
Activity Number:
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597
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #309526 |
Title:
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Hypothesis-Testing for Curves Comparison: Permutation Approach vs. Trigonometric Expansion Methods
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Author(s):
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Livio Corain*+ and Viatcheslav B. Melas and Andrey Pepelyshev and Luigi Salmaso
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Companies:
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University of Padova and St. Petersburg State University and RWTH Aachen University and University of Padova
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Keywords:
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basis function approximation ;
functional data ;
nonparametric combination ;
permutation tests
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Abstract:
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Important inferential problems usually occur when the data of interest are a collection of scalars or vectors which can be viewed as samples drawn from population of curves or trajectories. This type of data can be viewed as either longitudinal data from repeated measures on the same units/subjects or observations called functional data. From a critical review on hypothesis testing solutions for curves comparison proposed either by the functional data analysis - FDA and by the longitudinal data analysis - LDA literature it appears that there are basically two main approaches: overall tests and basis function approximation solutions. The first class of procedures are concerned with developing a global test which compares the population of curves using a suitable test statistic defined in the whole domain of the functional response while the second one is aimed at testing on the equality of the coefficients from a basis function approximation. The purpose of this work is to theoretically and numerically compare two specific solutions within the above mentioned two approaches: the time-to-time permutation test and a new solution inspired on the trigonometric expansion methods.
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Authors who are presenting talks have a * after their name.
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