Small-sample estimation of negative binomial dispersion, with applications to SAGE data.
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.
Robinson
Mark D
MD
Bioinformatics 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
Smyth
Gordon K
GK
D015982:Bias; D016010:Binomial Distribution; D001699:Biometry / Q000379:methods; D020224:Expressed Sequence Tags; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D015723:Gene Library; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods / Q000706:statistics & numerical data; D016013:Likelihood Functions; D012333:RNA, Messenger / Q000032:analysis*; D012044:Regression Analysis; D012107:Research Design / Q000706:statistics & numerical data; D018401:Sample Size; D013269:Stochastic Processes; D014894:Weights and Measures
D012333:RNA, Messenger