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December 2009 Nonparametric Bayesian multiple testing for longitudinal performance stratification
James G. Scott
Ann. Appl. Stat. 3(4): 1655-1674 (December 2009). DOI: 10.1214/09-AOAS252

Abstract

This paper describes a framework for flexible multiple hypothesis testing of autoregressive time series. The modeling approach is Bayesian, though a blend of frequentist and Bayesian reasoning is used to evaluate procedures. Nonparametric characterizations of both the null and alternative hypotheses will be shown to be the key robustification step necessary to ensure reasonable Type-I error performance. The methodology is applied to part of a large database containing up to 50 years of corporate performance statistics on 24,157 publicly traded American companies, where the primary goal of the analysis is to flag companies whose historical performance is significantly different from that expected due to chance.

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James G. Scott. "Nonparametric Bayesian multiple testing for longitudinal performance stratification." Ann. Appl. Stat. 3 (4) 1655 - 1674, December 2009. https://doi.org/10.1214/09-AOAS252

Information

Published: December 2009
First available in Project Euclid: 1 March 2010

zbMATH: 1184.62156
MathSciNet: MR2752152
Digital Object Identifier: 10.1214/09-AOAS252

Keywords: Bayesian model selection , financial time series , multiple testing , nonparametric Bayes

Rights: Copyright © 2009 Institute of Mathematical Statistics

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Vol.3 • No. 4 • December 2009
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