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Archive for the ‘empirical Bayes’ Category

Evidential unification of confidence and empirical Bayes methods

1 March 2019 Leave a comment

How to choose features or p values for empirical Bayes estimation of the local false discovery rate

1 December 2018 Leave a comment

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

R package for estimating local false discovery rates using empirical Bayes methods

15 October 2018 Leave a comment

Lower the statistical significance threshold to 0.005—or 0.001?

1 October 2018 Leave a comment

Should the default significance level be changed from 0.05 to 0.005?

1 July 2018 Leave a comment

My comments in this discussion of “Redefine statistical significance”:

The call for smaller significance levels cannot be based only on mathematical arguments that p values tend to be much lower than posterior probabilities, as Andrew Gelman and Christian Robert pointed out in their comment (“Revised evidence for statistical standards”).

In the rejoinder, Valen Johnson made it clear that the call is also based on empirical findings of non-reproducible research results. How many of those findings are significant at the 0.005 level? Should meta-analysis have a less stringent standard?

“Irreplicable results can’t possibly add empirical clout to the mathematical argument unless it is already known or assumed to be caused by a given cut-off, and further, that lowering it would diminish those problems.”

The preprint cites empirical results to support its use of the 1:10 prior odds. If that is in fact a reliable estimate of the prior odds for the reference class of previous studies, then, in the absence of other relevant information, it would be reasonable to use as input for Bayes’s theorem.

John Byrd asks, “Is 1:10 replicable?” Is it important to ask whether a 1:1 prior odds can be rejected at the 0.005 significance level?

END

An R package to transform false discovery rates to posterior probability estimates

1 May 2018 Leave a comment

There are many estimators of false discovery rate. In this package we compute the Nonlocal False Discovery Rate (NFDR) and the estimators of local false discovery rate: Corrected False discovery Rate (CFDR), Re-ranked False Discovery rate (RFDR) and the blended estimator.

Source: CRAN – Package CorrectedFDR

LFDR.MLE-package function | R Documentation

1 March 2018 Leave a comment

Suite of R functions for the estimation of the local false discovery rate (LFDR) using Type II maximum likelihood estimation (MLE):

LFDR.MLE-package function | R Documentation

Categories: empirical Bayes, software