I was impressed by Pierre-Antoine Kremp’s open-source poll aggregator and election forecaster (all in R and Stan with an automatic data feed!) so I wrote to Kremp: I was thinking it could be fun to compute probability of decisive vote by state, as in this paper. This can be done with some not difficult but […]

**Multilevel Modeling**category.

## Some modeling and computational ideas to look into

Can we implement these in Stan? Marginally specified priors for non-parametric Bayesian estimation (by David Kessler, Peter Hoff, and David Dunson): Prior specification for non-parametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. A statistician is unlikely to have informed opinions about all aspects of […]

## Mister P can solve problems with survey weighting

It’s tough being a blogger who’s expected to respond immediately to topics in his area of expertise. For example, here’s Scott “fraac” Adams posting on 8 Oct 2016, post titled “Why Does This Happen on My Vacation? (The Trump Tapes).” After some careful reflection, Adams wrote, “My prediction of a 98% chance of Trump winning […]

## Trump +1 in Florida; or, a quick comment on that “5 groups analyze the same poll” exercise

Nate Cohn at the New York Times arranged a comparative study on a recent Florida pre-election poll. He sent the raw data to four groups (Charles Franklin; Patrick Ruffini; Margie Omero, Robert Green, Adam Rosenblatt; and Sam Corbett-Davies, David Rothschild, and me) and asked each of us to analyze the data how we’d like to […]

## Q: “Is A 50-State Poll As Good As 50 State Polls?” A: Use Mister P.

Jeff Lax points to this post from Nate Silver and asks for my thoughts. In his post, Nate talks about data quality issues of national and state polls. It’s a good discussion, but the one thing he unfortunately doesn’t talk about is multilevel regression and poststratification (or see here for more). What you want to […]

## Polling in the 21st century: There ain’t no urn

David Rothschild writes: The Washington Post (WaPo) utilized Survey Monkey (SM) to survey 74,886 registered voters in all 50 states on who they would vote for in the upcoming election. I am very excited about the work, because I am a huge proponent of advancing polling methodology, but the methodological explanation and data detail bring […]

## Fast CAR: Two weird tricks for fast conditional autoregressive models in Stan

Max Joseph writes: Conditional autoregressive (CAR) models are popular as prior distributions for spatial random effects with areal spatial data. Historically, MCMC algorithms for CAR models have benefitted from efficient Gibbs sampling via full conditional distributions for the spatial random effects. But, these conditional specifications do not work in Stan, where the joint density needs […]

## Publication bias occurs within as well as between projects

Kent Holsinger points to this post by Kevin Drum entitled, “Publication Bias Is Boring. You Should Care About It Anyway,” and writes: I am an evolutionary biologist, not a psychologist, but this article describes a disturbing Scenario concerning oxytocin research that seems plausible. It is also relevant to the reproducibility/publishing issues you have been discussing […]

## Hey pollsters! Poststratify on party ID, or we’re all gonna have to do it for you.

Alan Abramowitz writes: In five days, Clinton’s lead increased from 5 points to 12 points. And Democratic party ID margin increased from 3 points to 10 points. No, I don’t think millions of voters switched to the Democratic party. I think Democrats are were just more likely to respond in that second poll. And, remember, […]

## His varying slopes don’t seem to follow a normal distribution

Bruce Doré writes: I have a question about multilevel modeling I’m hoping you can help with. What should one do when random effects coefficients are clearly not normally distributed (i.e., coef(lmer(y~x+(x|id))) )? Is this a sign that the model should be changed? Or can you stick with this model and infer that the assumption of […]

## Taking Bayesian Inference Seriously [my talk tomorrow at Harvard conference on Big Data]

Mon 22 Aug, 9:50am, at Harvard Science Center Hall A: Taking Bayesian Inference Seriously Over the years I have been moving toward the use of informative priors in more and more of my applications. I will discuss several examples from theory, application, and computing where traditional noninformative priors lead to disaster, but a little bit […]

## Modeling correlation of issue attitudes and partisanship within states

John Kuk writes: I have taught myself multilevel modeling using your book and read your work with Delia Baldassarri about partisanship and issue alignment. I have a question about related to these two works. I want to find the level of correlation between partisanship and issues at the state level. Your work with Professor Baldassarri […]

## Documented forking paths in the Competitive Reaction Time Task

Baruch Eitan writes: This is some luscious garden of forking paths. Indeed. Here’s what Malte Elson writes at the linked website: The Competitive Reaction Time Task, sometimes also called the Taylor Aggression Paradigm (TAP), is one of the most commonly used tests to purportedly measure aggressive behavior in a laboratory environment. . . . While […]

## Smooth poll aggregation using state-space modeling in Stan, from Jim Savage

Jim Savage writes: I just saw your post on poll bounces; have been thinking the same myself. Why are the poll aggregators so jumpy about new polls? Annoyed, I put together a poll aggregator that took a state-space approach to the unobserved preferences; nothing more than the 8 schools (14 polls?) example with a time-varying […]

## Guy Fieri wants your help! For a TV show on statistical models for real estate

I got the following email from David Mulholland: I’m a producer at Citizen Pictures where we produce Food Network’s “Diners, Dives and Drive-Ins” and Bravo’s digital series, “Going Off The Menu,” among others. A major network is working with us to develop a show that pits “data” against a traditional real estate agent to see […]

## All maps of parameter estimates remain misleading

Roland Rau writes: After many years of applying frequentist statistical methods in mortality research, I just began to learn about the application of Bayesian methods in demography. Since I also wanted to change a part of my research focus on spatial models, I discovered your 1999 paper with Phil Price, All maps of parameter estimates […]

## What recommendations to give when a medical study is not definitive (which of course will happen all the time, especially considering that new treatments should be compared to best available alternatives, which implies that most improvements will be incremental at best)

Simon Gates writes: I thought you might be interested in a recently published clinical trial, for potential blog material. It picks up some themes that have cropped in recent months. Also, it is important for the way statistical methods influence what can be life or death decisions. The OPPTIMUM trial (http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(16)00350-0/abstract) evaluated use of vaginal progesterone […]