I had an interesting discussion with Peter Dorman (whose work on assessing the value of a life we discussed in this space a few years ago). The conversation started when Peter wrote me about his recent success using hierarchical modeling for risk analysis. He wrote, “Where have they [hierarchical models] been all my life? In […]

**Multilevel Modeling**category.

## Stan World Cup update

The other day I fit a simple model to estimate team abilities from World Cup outcomes. I fit the model to the signed square roots of the score differentials, using the square root on the theory that when the game is less close, it becomes more variable. 0. Background As you might recall, the estimated […]

## Stan goes to the World Cup

I thought it would be fun to fit a simple model in Stan to estimate the abilities of the teams in the World Cup, then I could post everything here on the blog, the whole story of the analysis from beginning to end, showing the results of spending a couple hours on a data analysis. […]

## Chicago alert: Mister P and Stan to be interviewed on WBEZ today (Fri) 3:15pm

Niala Boodho on the Afternoon Shift will be interviewing Yair and me about our age-period-cohort extravaganza which became widely-known after being featured in this cool interactive graph by Amanda Cox in the New York Times. And here’s the interview. The actual paper is called The Great Society, Reagan’s revolution, and generations of presidential voting and […]

## “P.S. Is anyone working on hierarchical survival models?”

Someone who wishes to remain anonymous writes: I’m working on building a predictive model (not causal) of the onset of diabetes mellitus using electronic medical records from a semi-panel of HMO patients. The dependent variable is blood glucose level. The unit of analysis is the patient visit to a network doctor or hospitalization in a […]

## Comment of the week

This one, from DominikM: Really great, the simple random intercept – random slope mixed model I did yesterday now runs at least an order of magnitude faster after installing RStan 2.3 this morning. You are doing an awesome job, thanks a lot!

## Combining forecasts: Evidence on the relative accuracy of the simple average and Bayesian model averaging for predicting social science problems

Andreas Graefe sends along this paper (with Helmut Kuchenhoff, Veronika Stierle, and Bernhard Riedl) and writes: We summarize prior evidence from the field of economic forecasting and find that the simple average was more accurate than Bayesian model averaging in three of four studies; on average, the error of BMA was 6% higher than the […]

## Spring forward, fall back, drop dead?

Antonio Rinaldi points me to a press release describing a recent paper by Amneet Sandhu, Milan Seth, and Hitinder Gurm, where I got the above graphs (sorry about the resolution, that’s the best I could do). Here’s the press release: Data from the largest study of its kind in the U.S. reveal a 25 percent […]

## Regression and causality and variable ordering

Bill Harris wrote in with a question: David Hogg points out in one of his general articles on data modeling that regression assumptions require one to put the variable with the highest variance in the ‘y’ position and the variable you know best (lowest variance) in the ‘x’ position. As he points out, others speak […]

## Identifying pathways for managing multiple disturbances to limit plant invasions

Andrew Tanentzap, William Lee, Adrian Monks, Kate Ladley, Peter Johnson, Geoffrey Rogers, Joy Comrie, Dean Clarke, and Ella Hayman write: We tested a multivariate hypothesis about the causal mechanisms underlying plant invasions in an ephemeral wetland in South Island, New Zealand to inform management of this biodiverse but globally imperilled habitat. . . . We […]