Nonparametric Bayesian multiple testing for longitudinal performance stratification

Details Nonparametric Bayesian multiple testing for longitudinal performance stratification Nonparametric Bayesian multiple testing for longitudinal performance stratification

2010-09-29

arxiv.org/abs/1009.5869v1

Annals of Applied Statistics 2009, Vol. 3, No. 4, 1655-1674

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Published in at http://dx.doi.org/10.1214/09-AOAS252 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/09-AOAS252

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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