Archive
How to choose features or p values for empirical Bayes estimation of the local false discovery rate
F. Abbas-Aghababazadeh, M. Alvo, and D. R. Bickel, “Estimating the local false discovery rate via a bootstrap solution to the reference class problem,” PLoS ONE 13, e0206902 (2018) | full text | 2016 preprint
Estimates of the local false discovery rate based on prior information: Application to GWAS
A. Karimnezhad and D. R. Bickel, “Incorporating prior knowledge about genetic variants into the analysis of genetic association data: An empirical Bayes approach,” Working Paper, University of Ottawa, deposited in uO Research at http://hdl.handle.net/10393/34889 (2016). 2016 preprint
Adaptively selecting an empirical Bayes reference class
F. A. Aghababazadeh, M. Alvo, and D. R. Bickel, “Estimating the local false discovery rate via a bootstrap solution to the reference class problem,” Working Paper, University of Ottawa, deposited in uO Research at http://hdl.handle.net/10393/34295 (2016). 2016 preprint
MLE of the local FDR
Y. Yang, F. A. Aghababazadeh, and D. R. Bickel, “Parametric estimation of the local false discovery rate for identifying genetic associations,” IEEE/ACM Transactions on Computational Biology and Bioinformatics 10, 98-108 (2013). 2010 version | Slides
Software for local false discovery rate estimation
LFDR-MLE is a suite of R functions for the estimation of local false discovery rates by maximum likelihood under a two-group parametric mixture model of test statistics.
Quantifying evidence for genetic association
Y. Yang and D. R. Bickel, “Minimum description length and empirical Bayes methods of identifying SNPs associated with disease,” Technical Report, Ottawa Institute of Systems Biology, COBRA Preprint Series, Article 74, available at biostats.bepress.com/cobra/ps/art74 (2010).
This manuscript adapts two new evidential, information-theoretic methods to the problem of detecting SNPs associated with disease on the basis of genome-wide association data. Both an application to coronary artery disease and an extensive set of simulation studies indicate that these parametric methods tend to be more reliable than a popular semi-parametric approach to estimating local false discovery rates. In addition, the paper reports that one of the two novel methods performs better than the other.
The abstract and the discussion section of the preprint provide more detailed summaries.
David Bickel, statistician 
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