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Small-sample estimation of negative binomial dispersion, with applications to SAGE data
Mark D Robinson1, Gordon K Smyth
1Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, and Department of Medical Biology, The University of Melbourne, Parkville, Victoria 3010, Australia. mrobinson@wehi.edu.au
Abstract:
We derive a quantile-adjusted conditional maximum likelihood estimator for the dispersion parameter of the negative binomial distribution and compare its performance, in terms of bias, to various other methods. Our estimation scheme outperforms all other methods in very small samples, typical of those from serial analysis of gene expression studies, the motivating data for this study. The impact of dispersion estimation on hypothesis testing is studied. We derive an "exact" test that outperforms the standard approximate asymptotic tests.
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