Many of you have heard of this article (with Aki Vehtari) already—we wrote the first version in 2020, then did some revision for its publication in the Journal of the American Statistical Association. But the journal is not open-access so … Continue reading
Here it is: N.S.: In the past few years, you’ve become sort of the scourge of bad statistical papers. I’ve heard it said that the scariest seven words in academia are: “Andrew Gelman just blogged about your paper”. Do you … Continue reading
A colleague sent me an email with the above title and the following content: We were talking about Jamaica childhood intervention study. The Science paper on returns to the intervention 20 years later found a 25% increase but an earlier … Continue reading
That’s something that Charles Margossian said in a work meeting today, referring to the idea that if a certain software feature (embedded Laplace in Stan) were made more flexible, that would complicate the API. That was a good point, and … Continue reading
A journalist pointed me to a recent research article, “The impact of a poverty reduction intervention on infant brain activity,” which stated: Here, we report estimates of the causal impact of a poverty reduction intervention on brain activity in the … Continue reading
Torleif Halkjelsvik from the Norwegian Institute of Public Health writes: Norway has very good register data (education/income/health/drugs/welfare/etc.) but it is difficult to obtain complete tables at the population level. It is however easy to get independent tables from different registries … Continue reading
The post is by Leonardo Egidi. This Bayesian fairy tale starts in July 2016 and will reveal some mysteries of the magical world of mixtures. Opening: the airport idea Once upon a time a young Italian statistician was dragging his … Continue reading
This post is by Yuling, not Andrew. We have been talking about how Bayesian inference can be flawed. Particularly, we have argued that discrete model comparison and model averaging using marginal likelihood can often go wrong, unless you have a … Continue reading
Hannes Margraf writes: I would like your opinion on an emerging practice in machine learning for materials science. The idea is to find empirical relationships between a complex material property (say the critical temperature of a superconductor) and simple descriptors … Continue reading
This is Jessica. Not too long ago, I wrote about research on when claims in machine learning (ML)-oriented research are not reproducible, where I found it useful to try to contrast the nature of claims, and threats to their validity, … Continue reading
I used this expression the other day in Lauren’s seminar and she told me she’d never heard it before, which surprised me because I feel like I’ve been saying it for awhile, so I googled *statmodeling bayesian cringe* but nothing … Continue reading
Someone who would like to remain anonymous writes: I have read your blog posts discussing the benefits of Bayesian inference and partial pooling from a multilevel modeling approach. Recently, I’ve begun thinking about designing a simulation to prove to myself … Continue reading
Yuling Yao, Gregor Pirš, Aki Vehtari, and I write: Stacking is a widely used model averaging technique that asymptotically yields optimal predictions among linear averages. We show that stacking is most effective when model predictive performance is heterogeneous in inputs, … Continue reading
To coincide with the publication of our article, A Proposal for Informative Default Priors Scaled by the Standard Error of Estimates, Erik van Zwet sends along an explainer. Here’s Erik: 1 Set-up This note is meant as a quick explainer … Continue reading
Aki and I put together this listsicle to accompany our recent paper on the most important statistical ideas of the top 50 years. Kim Martineau at Columbia, who suggested making this list, also had the idea that youall might have … Continue reading
Something came up in comments today that I’d like to follow up on. In our earlier post, I brought up an example: If you’re modeling attitudes about gun control, think hard about what state-level predictors to include. My colleagues and … Continue reading
For the Boston chapter of the American Statistical Association, I’ll be speaking on this paper with Aki: What are the most important statistical ideas of the past 50 years? We argue that the most important statistical ideas of the past … Continue reading
(This post is by Yuling) Gregor Pirš, Aki, Andrew, and I wrote: Stacking is a widely used model averaging technique that yields asymptotically optimal predictions among linear averages. We show that stacking is most effective when the model predictive performance … Continue reading
Ruth Etzioni, Micha Mandel, Roman Gulati wrote a new book that I really like. Here are the chapters: 1 Statistics and Health Data 1.1 Introduction 1.2 Statistics and Organic Statistics 1.3 Statistical Methods and Models 1.4 Health Care Data 1.5 … Continue reading
Alexey Guzey points to this post by Scott Alexander and this research article by Elisabetta Patorno, Robert Glynn, Raisa Levin, Moa Lee, and Krista Huybrechts, and writes: I [Guzey] am extremely skeptical of anything that relies on adjusting for confounders … Continue reading