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Comparison of two populations of curves with an application in neuronal data analysis
Author(s) -
Behseta Sam,
Chenouri Shojaeddin
Publication year - 2011
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4192
Subject(s) - nonparametric statistics , statistic , test statistic , statistical hypothesis testing , parametric statistics , bayesian probability , statistics , mathematics , functional data analysis , histogram , akaike information criterion , computer science , pattern recognition (psychology) , artificial intelligence , image (mathematics)
Abstract Often in neurophysiological studies, scientists are interested in testing hypotheses regarding the equality of the overall intensity functions of a group of neurons when recorded under two different experimental conditions. In this paper, we consider such a hypothesis testing problem. We propose two test statistics: a parametric test similar to the modified Hotelling's T 2 statistic of Behseta and Kass ( Statist. Med . 2005; 24:3523–3534), as well as a nonparametric one similar to the spatial signed‐rank test statistic of Möttönen and Oja ( J. Nonparametric Statist . 1995; 5:201–213). We implement these tests on smooth curves obtained via fitting Bayesian Adaptive Regression Splines (BARS) to the intensity functions of neuronal Peri‐Stimulus Time Histograms. Through simulation, we show that the powers of our proposed tests are extremely high even when the number of sampled neurons and the number of trials per neuron are small. Finally, we apply our methods on a group of motor cortex neurons recorded during a reaching task. Copyright © 2011 John Wiley & Sons, Ltd.