Nonparametric Bayesian multiple testing for longitudinal performance stratification

Nonparametric Bayesian multiple testing for longitudinal performance stratification

James G. Scott

Duke University

April 2008

This paper describes a framework for Bayesian multiple-hypothesis testing of autoregressive time series. 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. Issues of prior specification are considered in detail. 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; the primary goal of the analysis is to flag companies whose historical performance is significantly different from that expected due to chance.

Keywords: multiple comparisons; Bayesian model selection; nonparametric Bayes; corporate performance.


The manuscript is in available in PDF format.


Cite as:

@Article{Scott:2009,
      Author = "Scott, James G.",
       Title = "Nonparametric Bayesian multiple testing for longitudinal performance stratification", 
        Year = 2009,
	journal = {The Annals of Applied Statistics},
	note = {to appear}
}