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The Bayesian t-Test and Beyond

  • Mithat Gönen
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 620)

Abstract

In this chapter we will explore Bayesian alternatives to the t-test. We saw in Chapter 1 how t-test can be used to test whether the expected outcomes of the two groups are equal or not. In Chapter 3 we saw how to make inferences from a Bayesian perspective in principle. In this chapter we will put these together to develop a Bayesian procedure for a t-test. This procedure depends on the data only through the t-statistic. It requires prior inputs and we will discuss how to assign them. We will use an example from a microarray study as to demonstrate the practical issues. The microarray study is an important application for the Bayesian t-test as it naturally brings up the question of simultaneous t-tests. It turns out that the Bayesian procedure can easily be extended to carry several t-tests on the same data set, provided some attention is paid to the concept of the correlation between tests.

Key words

Two-sample comparisons prior correlations multivariate testing simultaneous inferences multiple testing 

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Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Mithat Gönen
    • 1
  1. 1.Department of Epidemiology and BiostatisticsMemorial Sloan-Kettering Cancer CenterNew YorkUSA

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