Jared writes: I gave a talk at the Washington DC Conference about six different tools for fitting lasso models. You’ll be happy to see that rstanarm outperformed the rest of the methods. That’s about 60 more slides than I would’ve … Continue reading
[cat picture] Someone on the users list asked about lasso regression in Stan, and Ben replied: In the rstanarm package we have stan_lm(), which is sort of like ridge regression, and stan_glm() with family = gaussian and prior = laplace() … Continue reading
Tom Swartz writes: I am a graduate student at Oxford with a background in economics and on the side am teaching myself more statistics and machine learning. I’ve been following your blog for some time and recently came across this … Continue reading
Lasso and me For a long time I was wrong about lasso. Lasso (“least absolute shrinkage and selection operator”) is a regularization procedure that shrinks regression coefficients toward zero, and in its basic form is equivalent to maximum penalized likelihood … Continue reading
Earlier today we posted something on our recent paper with Erik van Zwet et al., “A New Look at P Values for Randomized Clinical Trials.” The post had the provocative title, “Bayesians moving from defense to offense,” and indeed that … Continue reading
Carl Morris recently passed away, so I will repost this article from 2015: When Carl Morris came to our department in 1989, I and my fellow students were so excited. We all took his class. The funny thing is, though, … Continue reading
Last year I wrote about the value of testing observable consequences of a randomized experiment having occurred as planned. For example, if the randomization was supposedly Bernoulli(1/2), you can check that the number of units in treatment and control in … 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
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
Something came up where I realized I was wrong. It wasn’t a mathematical error; it was a statistical model that was misleading when I tried to align it with reality. And this made me realize that there was something I … 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
This post is by Eric. This Wednesday, at 12 pm ET, Tamara Broderick is stopping by to talk to us about pairwise interactions in high dimensions. You can register here. Abstract Discovering interaction effects on a response of interest is … Continue reading
Rob Tibshirani writes: About 9 years ago I emailed you about our new significance result for the lasso. You wrote about in your blog. For some reason I never saw that full blog until now. I do remember the Stanford-Berkeley … Continue reading
OK, usually our posts are on 6-month delay. Sometimes they get bumped a couple times and don’t appear until more than a year after I’ve written them. Other times I write something topical and I’ll schedule it for sometime during … Continue reading
I get frustrated when people use overly restrictive definitions of something they don’t like. Here’s an example of an overly restrictive definition that got me thinking about all this. Larry Wasserman writes (as reported by Deborah Mayo): I wish people … Continue reading
I originally gave this post the title, “Stigler: The Changing History of Robustness,” but then I was afraid nobody would read it. In the current environment of Move Fast and Break Things, not so many people care about robustness. Also, … Continue reading
It might not be common courtesy of this blog to make comments on a very-recently-arxiv-ed paper. But I have seen two copies of this paper entitled “how good is the Bayes posterior in deep neural networks really” left on the tray … Continue reading
I (Yuling) read this new book Machine Learning Under a Modern Optimization Lens (by Dimitris Bertsimas and Jack Dunn) after I grabbed it from Andrew’s desk. Apparently machine learning is now such a wide-ranging area that we have to access … Continue reading
Here’s what’s scheduled for the next six months: This is a great example for a statistics class, or a class on survey sampling, or a political science class How to read (in quantitative social science). And by implication, how to … Continue reading
A few months ago I sent the following message to some people: Dear philosophically-inclined colleagues: I’d like to organize an online discussion of Deborah Mayo’s new book. The table of contents and some of the book are here at Google … Continue reading