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Archive of posts filed under the Multilevel Modeling category.

My talk at MIT this Thursday

When I was a student at MIT, there was no statistics department. I took a statistics course from Stephan Morgenthaler and liked it. (I’d already taken probability and stochastic processes back at the University of Maryland; my instructor in the latter class was Prof. Grace Yang, who was super-nice. I couldn’t follow half of what […]

Collaborative filtering, hierarchical modeling, and . . . speed dating

Jonah Sinick posted a few things on the famous speed-dating dataset and writes: The main element that I seem to have been missing is principal component analysis of the different rating types. The basic situation is that the first PC is something that people are roughly equally responsive to, while people vary a lot with […]

Social networks spread disease—but they also spread practices that reduce disease

I recently posted on the sister blog regarding a paper by Jon Zelner, James Trostle, Jason Goldstick, William Cevallos, James House, and Joseph Eisenberg, “Social Connectedness and Disease Transmission: Social Organization, Cohesion, Village Context, and Infection Risk in Rural Ecuador.” Zelner follows up: This made me think of my favorite figure from this paper, which […]

Statistical analysis on a dataset that consists of a population

This is an oldie but a goodie. Donna Towns writes: I am wondering if you could help me solve an ongoing debate? My colleagues and I are discussing (disagreeing) on the ability of a researcher to analyze information on a population. My colleagues are sure that a researcher is unable to perform statistical analysis on […]

Instead of worrying about multiple hypothesis correction, just fit a hierarchical model.

Pejman Mohammadi writes: I’m concerned with a problem in multiple hypothesis correction and, despite having read your article [with Jennifer and Masanao] on not being concerned about it, I was hoping I could seek your advice. Specifically, I’m interested in multiple hypothesis testing problem in cases when the test is done with a discrete finite […]

Why I don’t use the terms “fixed” and “random” (again)

A couple months ago we discussed this question from Sean de Hoon: In many cross-national comparative studies, mixed effects models are being used in which a number of slopes are fixed and the slopes of one or two variables of interested are allowed to vary across countries. The aim is often then to explain the […]

They don’t fit into their categories

I was reading the newspaper today and came across an article entitled, “Black Holes Inch Ahead to Violent Cosmic Union.” No big deal, except that it was in the National section. Not the International. This reminded me of other examples where items didn’t fit into their categories, for example when Slate magazine published an article, […]

Using y.bar to predict y: What’s that all about??

Toon Kuppens writes: After a discussion on a multilevel modeling mailing list, I came across this one-year-old blog post written by you. You might be interested to know that in social psychology, taking the aggregate outcome variable to predict the outcome variable has been used as a test of ‘convergence’, the phenomenon that people’s responses […]

My talk tomorrow (Thurs) at MIT political science: Recent challenges and developments in Bayesian modeling and computation (from a political and social science perspective)

It’s 1pm in room E53-482. I’ll talk about the usual stuff (and some of this too, I guess).

VB-Stan: Black-box black-box variational Bayes

Alp Kucukelbir, Rajesh Ranganath, Dave Blei, and I write: We describe an automatic variational inference method for approximating the posterior of differentiable probability models. Automatic means that the statistician only needs to define a model; the method forms a variational approximation, computes gradients using automatic differentiation and approximates expectations via Monte Carlo integration. Stochastic gradient […]