A Bayesian Nonparametric Method for Prediction in EST Analysis

Antonio Lijoi (), Ramsés H. Mena () and Igor Prünster ()

ICER Working Papers - Applied Mathematics Series from ICER - International Centre for Economic Research

Abstract: In this work we propose a Bayesian nonparametric approach for tackling statistical problems related to EST surveys. In particular, we provide estimates for: a) the coverage, defined as the proportion of unique genes in the library represented in the given sample of reads; b) the number of new unique genes to be observed in a future sample; c) the discovery rate of new genes as a function of the future sample size. The Bayesian nonparametric model we adopt conveys, in a statistically rigorous way, the available information into prediction. Our proposal has appealing properties over frequentist nonparametric methods, which become unstable when prediction is required for large future samples. EST libraries studied in Susko and Roger (2004), with frequentist methods, are analyzed in detail.

Pages: 15 pages
Date: 2007-03
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:icr:wpmath:16-2007

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