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Archive for the ‘Contributions’ Category

Pre-data insights update priors via Bayes’s theorem

1 September 2018 Leave a comment

How to adjust statistical inferences for the simplicity of distributions

1 August 2018 Leave a comment

An idealized Cromwell’s principle

1 June 2018 Leave a comment

Cromwell’s principle idealized under the theory of large deviations

Seminar, Statistics and Probability Research Group, University of Ottawa

Ottawa, Ontario

April 27, 2018

David R. Bickel

University of Ottawa

Abstract. Cromwell’s principle requires that the prior probability that one’s assumptions are incorrect is greater than 0. That is relevant to Bayesian model checking since diagnostics often reveal that prior distributions require revision, which would be impossible under Bayes’s theorem if those priors were 100% probable. The idealized Cromwell’s principle makes the probability of making incorrect assumptions arbitrarily small. Enforcing that principle under large deviations theory leads to revising Bayesian models by maximum entropy in wide generality.

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

Inference to the best explanation of the evidence

1 February 2018 Leave a comment

The p value and Bayesian methods have well known drawbacks when it comes to measuring the strength of the evidence supporting one hypothesis over another. To overcome those drawbacks, this paper proposes an alternative method of quantifying how much support a hypothesis has from evidence consisting of data.

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D. R. Bickel, “The strength of statistical evidence for composite hypotheses: Inference to the best explanation,” Statistica Sinica 22, 1147-1198 (2012). Full article2010 version

The special law of likelihood has many advantages over more commonly used approaches to measuring the strength of statistical evidence. However, it only can measure the support of a hypothesis that corresponds to a single distribution. The proposed general law of likelihood also can measure the extent to which the data support a hypothesis that corresponds to multiple distributions. That is accomplished by formalizing inference to the best explanation.

Read more…

How to make decisions using somewhat reliable posterior distributions

15 January 2018 Leave a comment
Categories: model checking, preprints