Category: Bayesian Statistics

Prior distributions for covariance matrices

Someone sent me a question regarding the inverse-Wishart prior distribution for covariance matrix, as it is the default in some software he was using. Inverse-Wishart does not make sense for prior distribution; it has problems because the shape and scale are tangled. See this paper, “Visualizing Distributions of Covariance Matrices,” by Tomoki Tokuda, Ben Goodrich, […]

The post Prior distributions for covariance matrices appeared first on Statistical Modeling, Causal Inference, and Social Science.

A parable regarding changing standards on the presentation of statistical evidence

Now, the P-value Sneetches Had tables with stars. The Bayesian Sneetches Had none upon thars. Those stars weren’t so big. They were really so small. You might think such a thing wouldn’t matter at all. But, because they had stars, all the P-value Sneetches Would brag, “We’re the best kind of Sneetch on the Beaches. […]

The post A parable regarding changing standards on the presentation of statistical evidence appeared first on Statistical Modeling, Causal Inference, and Social Science.

Bayes, statistics, and reproducibility: “Many serious problems with statistics in practice arise from Bayesian inference that is not Bayesian enough, or frequentist evaluation that is not frequentist enough, in both cases using replication distributions that do not make scientific sense or do not reflect the actual procedures being performed on the data.”

This is an abstract I wrote for a talk I didn’t end up giving. (The conference conflicted with something else I had to do that week.) But I thought it might interest some of you, so here it is: Bayes, statistics, and reproducibility The two central ideas in the foundations of statistics—Bayesian inference and frequentist […]

The post Bayes, statistics, and reproducibility: “Many serious problems with statistics in practice arise from Bayesian inference that is not Bayesian enough, or frequentist evaluation that is not frequentist enough, in both cases using replication distributions that do not make scientific sense or do not reflect the actual procedures being performed on the data.” appeared first on Statistical Modeling, Causal Inference, and Social Science.

“Economic predictions with big data” using partial pooling

Tom Daula points us to this post, “Economic Predictions with Big Data: The Illusion of Sparsity,” by Domenico Giannone, Michele Lenza, and Giorgio Primiceri, and writes: The paper wants to distinguish between variable selection (sparse models) and shrinkage/regularization (dense models) for forecasting with Big Data. “We then conduct Bayesian inference on these two crucial parameters—model […]

The post “Economic predictions with big data” using partial pooling appeared first on Statistical Modeling, Causal Inference, and Social Science.

2018: How did people actually vote? (The real story, not the exit polls.)

Following up on the post that we linked to last week, here’s Yair’s analysis, using Mister P, of how everyone voted. Like Yair, I think these results are much better than what you’ll see from exit polls, partly because the analysis is more sophisticated (MRP gives you state-by-state estimates in each demographic group), partly because […]

The post 2018: How did people actually vote? (The real story, not the exit polls.) appeared first on Statistical Modeling, Causal Inference, and Social Science.

Watch out for naively (because implicitly based on flat-prior) Bayesian statements based on classical confidence intervals! (Comptroller of the Currency edition)

Laurent Belsie writes: An economist formerly with the Consumer Financial Protection Bureau wrote a paper on whether a move away from forced arbitration would cost credit card companies money. He found that the results are statistically insignificant at the 95 percent (and 90 percent) confidence level. But the Office of the Comptroller of the Currency […]

The post Watch out for naively (because implicitly based on flat-prior) Bayesian statements based on classical confidence intervals! (Comptroller of the Currency edition) appeared first on Statistical Modeling, Causal Inference, and Social Science.

My two talks in Austria next week, on two of your favorite topics!

Innsbruck, 7 Nov 2018: The study of American politics as a window into understanding uncertainty in science We begin by discussing recent American elections in the context of political polarization, and we consider similarities and differences with European politics. We then discuss statistical challenges in the measurement of public opinion: inference from opinion polls with […]

The post My two talks in Austria next week, on two of your favorite topics! appeared first on Statistical Modeling, Causal Inference, and Social Science.

What does it mean to talk about a “1 in 600 year drought”?

Patrick Atwater writes: Curious to your thoughts on a bit of a statistical and philosophical quandary. We often make statements like this drought was a 1 in 400 year event but what do we really mean when we say that? In California for example there was an oft repeated line that the recent historic drought was […]

The post What does it mean to talk about a “1 in 600 year drought”? appeared first on Statistical Modeling, Causal Inference, and Social Science.

Fitting the Besag, York, and Mollie spatial autoregression model with discrete data

Rudy Banerjee writes: I am trying to use the Besag, York & Mollie 1991 (BYM) model to study the sociology of crime and space/time plays a vital role. Since many of the variables and parameters are discrete in nature is it possible to develop a BYM that uses an Integer Auto-regressive (INAR) process instead of […]

The post Fitting the Besag, York, and Mollie spatial autoregression model with discrete data appeared first on Statistical Modeling, Causal Inference, and Social Science.

He had a sudden cardiac arrest. How does this change the probability that he has a particular genetic condition?

Megan McArdle writes: I have a friend with a probability problem I don’t know how to solve. He’s 37 and just keeled over with sudden cardiac arrest, and is trying to figure out how to assess the probability that he has a given condition as his doctors work through his case. He knows I’ve been […]

The post He had a sudden cardiac arrest. How does this change the probability that he has a particular genetic condition? appeared first on Statistical Modeling, Causal Inference, and Social Science.