JSM 2005 - Toronto

Abstract #302851

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 258
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #302851
Title: Testing Equality of Two Functions Using BARS
Author(s): Sam Behseta*+ and Robert E. Kass
Companies: California State University, Bakersfield and Carnegie Mellon University
Address: 9001 Stockdale Hwy, Bakersfield, CA, 93311,
Keywords: Bayes factor ; Curve-fitting ; Functional data analysis ; Inhomogeneous Poisson process ; Neuronal data analysis ; Nonparametric regression
Abstract:

In this talk, we present two methods of testing the hypothesis of equality of two functions, in a generalized nonparametric regression framework using a recently-developed method called BARS (Bayesian Adaptive Regression Splines). Of particular interest is the special case of testing equality of two Poisson process intensity functions, which arises frequently in neurophysiological applications. The first method uses Bayes factors, and the second method uses a modified Hotelling test. Both methods are applied to the analysis of 347 motor cortical neurons and, for certain choices of test criteria, lead to the same conclusions for all but seven neurons. A small simulation study of power indicates the Bayes factor can be somewhat more powerful in small samples. The Hotelling-type test should be useful in screening large numbers of neurons for condition-related activity, while the Bayes factor will be especially helpful in assessing evidence in favor of the null hypothesis.

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Revised March 2005