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Archive of posts filed under the Multilevel Modeling category.

Index or indicator variables

Someone who doesn’t want his name shared (for the perhaps reasonable reason that he’ll “one day not be confused, and would rather my confusion not live on online forever”) writes: I’m exploring HLMs and stan, using your book with Jennifer Hill as my field guide to this new territory. I think I have a generally […]

If you get to the point of asking, just do it. But some difficulties do arise . . .

Nelson Villoria writes: I find the multilevel approach very useful for a problem I am dealing with, and I was wondering whether you could point me to some references about poolability tests for multilevel models. I am working with time series of cross sectional data and I want to test whether the data supports cross […]

Postdoc at Rennes on multilevel missing data imputation

Julie Josse sends along this job announcement: A post-doctoral position is available in the applied mathematics department of Agrocampus Rennes. The postdoc will be funded by the Henri Lebesgue Center (see if the application is selected. Applicants are expected to send their application before 31 March 2014. The research focus is on development of […]

Basketball Stats: Don’t model the probability of win, model the expected score differential.

Someone who wants to remain anonymous writes: I am working to create a more accurate in-game win probability model for basketball games. My idea is for each timestep in a game (a second, 5 seconds, etc), use the Vegas line, the current score differential, who has the ball, and the number of possessions played already […]

Xihong Lin on sparsity and density

I pointed Xihong Lin to this post from last month regarding Hastie and Tibshirani’s “bet on sparsity principle.” I argued that, in the worlds in which I work, in social and environmental science, every contrast is meaningful, even if not all of them can be distinguished from noise given a particular dataset. That is, I […]

Everything I need to know about Bayesian statistics, I learned in eight schools.

This post is by Phil. I’m aware that there are some people who use a Bayesian approach largely because it allows them to provide a highly informative prior distribution based subjective judgment, but that is not the appeal of Bayesian methods for a lot of us practitioners. It’s disappointing and surprising, twenty years after my initial experiences, […]

Postdoc involving pathbreaking work in MRP, Stan, and the 2014 election!

We’re working with polling company YouGov to track public opinion, state-by-state and district-by-district, during the 2014 campaign. We’ll be using multilevel regression and poststratification, and implementing it in Stan, and developing the necessary new parts of Stan to get this running scalably and efficiently. And we’ll be making the most detailed, up-to-date election forecasts. What […]

“Dogs are sensitive to small variations of the Earth’s magnetic field”

Two different people pointed me to this article by Vlastimil Hart et al. in the journal Frontiers in Zoology:


There’s lots of overlap but I put each paper into only one category.  Also, I’ve included work that has been published in 2013 as well as work that has been completed this year and might appear in 2014 or later.  So you can can think of this list as representing roughly two years’ work. Political […]

Using randomized incentives as an instrument for survey nonresponse?

I received the following question: Is there a classic paper on instrumenting for survey non-response? some colleagues in public health are going to carry out a survey and I wonder about suggesting that they build in a randomization of response-encouragement (e.g. offering additional $ to a subset of those who don’t respond initially). Can you […]