Current scientific techniques in genomics and image processing routinely produce hypothesis testing with hundreds or thousands of cases to consider simultaneously. This poses new difficulties for the statistician, but also opens new opportunities. In particular, it allows empirical estimation of an appropriate null hypothesis. The empirical null may be considerably more dispersed than the usual theoretical null distribution that would be used for any one case considered separately. An empirical Bayes analysis plan for this situation is developed, using a local version of the false discovery rate to examine the inference issues. Two genomics problems are used as examples to show the importance of correctly choosing the null hypothesis.
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Newton, M.A. (2008). Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis. In: Morris, C.N., Tibshirani, R. (eds) The Science of Bradley Efron. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-75692-9_21
Download citation
DOI: https://doi.org/10.1007/978-0-387-75692-9_21
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-75691-2
Online ISBN: 978-0-387-75692-9
eBook Packages: Mathematics and Statistics (R0)