55 citations · 98 across the 13 of their papers we have counts for
15 papers
An exploration of sequential Bayesian variable selection -- A comment on García-Donato et al. (2025). "Model uncertainty and missing data: An objective Bayesian perspective"
Sebastian Arnold, Alexander Ly
Our comment on García-Donato et al. (2025). "Model uncertainty and missing data: An objective Bayesian perspective" explores a further extension of the proposed methodology. Specif…
Empirical prior distributions for Bayesian meta-analyses of binary and time to event outcomes
František Bartoš, Willem M. Otte, Quentin F. Gronau +3
Bayesian model-averaged meta-analysis allows quantification of evidence for both treatment effectiveness and across-study heterogeneity . We use the Cochrane Database of Sys…
Evidential Calibration of Confidence Intervals
Samuel Pawel, Alexander Ly, Eric-Jan Wagenmakers
We present a novel and easy-to-use method for calibrating error-rate based confidence intervals to evidence-based support intervals. Support intervals are obtained from inverting B…
History and Nature of the Jeffreys-Lindley Paradox
Eric-Jan Wagenmakers, Alexander Ly
The Jeffreys-Lindley paradox exposes a rift between Bayesian and frequentist hypothesis testing that strikes at the heart of statistical inference. Contrary to what most current li…
Bayesian Model-Averaged Meta-Analysis in Medicine
František Bartoš, Quentin F. Gronau, Bram Timmers +3
We outline a Bayesian model-averaged meta-analysis for standardized mean differences in order to quantify evidence for both treatment effectiveness and across-study heterogenei…
Generic E-Variables for Exact Sequential k-Sample Tests that allow for Optional Stopping
Rosanne Turner, Alexander Ly, Peter Grünwald
We develop E-variables for testing whether two or more data streams come from the same source or not, and more generally, whether the difference between the sources is larger than…
Bayes Factors for Peri-Null Hypotheses
Alexander Ly, Eric-Jan Wagenmakers
A perennial objection against Bayes factor point-null hypothesis tests is that the point-null hypothesis is known to be false from the outset. We examine the consequences of approx…
The Anytime-Valid Logrank Test: Error Control Under Continuous Monitoring with Unlimited Horizon
J. ter Schure, M. F. Perez-Ortiz, A. Ly +1
We introduce the anytime-valid (AV) logrank test, a version of the logrank test that provides type-I error guarantees under optional stopping and optional continuation. The test is…
Default Bayes Factors for Testing the (In)equality of Several Population Variances
Fabian Dablander, Don van den Bergh, Eric-Jan Wagenmakers +1
Testing the (in)equality of variances is an important problem in many statistical applications. We develop default Bayes factor tests to assess the (in)equality of two or more popu…
Bayesian Rank-Based Hypothesis Testing for the Rank Sum Test, the Signed Rank Test, and Spearman's
Johnny van Doorn, Alexander Ly, Maarten Marsman +1
Bayesian inference for rank-order problems is frustrated by the absence of an explicit likelihood function. This hurdle can be overcome by assuming a latent normal representation t…
A Tutorial on Fisher Information
Alexander Ly, Maarten Marsman, Josine Verhagen +2
In many statistical applications that concern mathematical psychologists, the concept of Fisher information plays an important role. In this tutorial we clarify the concept of Fish…
Informed Bayesian T-Tests
Quentin F. Gronau, Alexander Ly, Eric-Jan Wagenmakers
Across the empirical sciences, few statistical procedures rival the popularity of the frequentist t-test. In contrast, the Bayesian versions of the t-test have languished in obscur…
A Tutorial on Bridge Sampling
Quentin F. Gronau, Alexandra Sarafoglou, Dora Matzke +7
The marginal likelihood plays an important role in many areas of Bayesian statistics such as parameter estimation, model comparison, and model averaging. In most applications, howe…
Bayesian Estimation of Kendall's tau Using a Latent Normal Approach
Johnny van Doorn, Alexander Ly, Maarten Marsman +1
The rank-based association between two variables can be modeled by introducing a latent normal level to ordinal data. We demonstrate how this approach yields Bayesian inference for…
Analytic Posteriors for Pearson's Correlation Coefficient
Alexander Ly, Maarten Marsman, Eric-Jan Wagenmakers
Pearson's correlation is one of the most common measures of linear dependence. Recently, Bernardo (2015) introduced a flexible class of priors to study this measure in a Bayesian s…