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Andrew Gelman

statmodeling.stat.columbia.edu

2016

“The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling”original ↗
10 Dec 2016
Temple Grandinoriginal ↗
9 Dec 2016
19 Things We Learned from the 2016 Electionoriginal ↗
8 Dec 2016
“So such markets were, and perhaps are, subject to bias from deep pocketed people who may be expressing preference more than actual expectation”original ↗
8 Dec 2016
“Dear Major Textbook Publisher”: A Rantoriginal ↗
7 Dec 2016
Hot hand 1, WSJ 0original ↗
6 Dec 2016
Data 1, NPR 0original ↗
6 Dec 2016
best algorithm EVER !!!!!!!!original ↗
6 Dec 2016
Using Stan in an agent-based model: Simulation suggests that a market could be useful for building public consensus on climate changeoriginal ↗
5 Dec 2016
Mighty oaks from little acorns groworiginal ↗
5 Dec 2016
Frustration with published results that can’t be reproduced, and journals that don’t seem to careoriginal ↗
4 Dec 2016
So little information to evaluate effects of dietary choicesoriginal ↗
3 Dec 2016
Some U.S. demographic data at zipcode level conveniently in Roriginal ↗
2 Dec 2016
Survey weighting and that 2% swingoriginal ↗
1 Dec 2016
How can you evaluate a research paper?original ↗
1 Dec 2016
An exciting new entry in the “clueless graphs from clueless rich guys” competitionoriginal ↗
30 Nov 2016
Interesting epi paper using Stanoriginal ↗
30 Nov 2016
“A bug in fMRI software could invalidate 15 years of brain research”original ↗
29 Nov 2016
OK, sometimes the concept of “false positive” makes sense.original ↗
28 Nov 2016
An election just happened and I can’t stop talking about itoriginal ↗
27 Nov 2016
Reminder: Instead of “confidence interval,” let’s say “uncertainty interval”original ↗
26 Nov 2016
Happiness formulasoriginal ↗
26 Nov 2016
Discussion on overfitting in cluster analysisoriginal ↗
25 Nov 2016
“Breakfast skipping, extreme commutes, and the sex composition at birth”original ↗
24 Nov 2016
Abraham Lincoln and confidence intervalsoriginal ↗
23 Nov 2016
I’m only adding new posts when they’re important . . . and this one’s really important.original ↗
23 Nov 2016
How best to partition data into test and holdout samples?original ↗
22 Nov 2016
a2original ↗
22 Nov 2016
Deep learning, model checking, AI, the no-homunculus principle, and the unitary nature of consciousnessoriginal ↗
21 Nov 2016
On deck this weekoriginal ↗
21 Nov 2016
“US Election Analysis 2016: Media, Voters and the Campaign”original ↗
20 Nov 2016
Unfinished (so far) draft blog postsoriginal ↗
20 Nov 2016
Individual and aggregate patterns in the Equality of Opportunity research projectoriginal ↗
19 Nov 2016
Know-it-all neuroscientist explains Trump’s election victory to the rest of usoriginal ↗
19 Nov 2016
Good news! PPNAS releases updated guidelines for getting a paper published in their social science divisionoriginal ↗
18 Nov 2016
Thinking more seriously about the design of exploratory studies: A manifestooriginal ↗
17 Nov 2016
Anti-immigration attitudes: they didn’t want a bunch of Hungarian refugees coming in the 1950soriginal ↗
17 Nov 2016
Only on the internet . . .original ↗
17 Nov 2016
Sniffing tears perhaps not as effective as claimedoriginal ↗
16 Nov 2016
Josh Miller hot hand talks in NYC and Pittsburgh this weekoriginal ↗
15 Nov 2016
“Men with large testicles”original ↗
15 Nov 2016
Kaggle Kernelsoriginal ↗
15 Nov 2016
Stan Webinar, Stan Classes, and StanConoriginal ↗
14 Nov 2016
Should scientists be allowed to continue to play in the sandbox after they’ve pooped in it?original ↗
14 Nov 2016
Goucher College is looking for a founding director of their Quantitative Reasoning Centeroriginal ↗
14 Nov 2016
Stan Case Studies: A good way to jump in to the languageoriginal ↗
14 Nov 2016
More on my paper with John Carlin on Type M and Type S errorsoriginal ↗
13 Nov 2016
En bra statistiklektionoriginal ↗
12 Nov 2016
The role of models and empirical work in political scienceoriginal ↗
12 Nov 2016
Election surprise, and Three ways of thinking about probabilityoriginal ↗
11 Nov 2016
David Rothschild and Sharad Goel called it (probabilistically speaking)original ↗
11 Nov 2016
Can a census-tract-level regression analysis untangle correlation between lead and crime?original ↗
11 Nov 2016
How effective (or counterproductive) is universal child care? Part 2original ↗
10 Nov 2016
Explanations for that shocking 2% shiftoriginal ↗
9 Nov 2016
A 2% swing: The poll-based forecast did fine (on average) in blue states; they blew it in the red statesoriginal ↗
9 Nov 2016
How effective (or counterproductive) is universal child care? Part 1original ↗
9 Nov 2016
Election forecasting updating error: We ignored correlations in some of our data, thus producing illusory precision in our inferencesoriginal ↗
9 Nov 2016
What if NC is a tie and FL is a close win for Clinton?original ↗
9 Nov 2016
Election updating software updateoriginal ↗
9 Nov 2016
Now that 7pm has come, what do we know?original ↗
9 Nov 2016
What might we know at 7pm?original ↗
8 Nov 2016
Blogging the election at Slateoriginal ↗
8 Nov 2016
Updating the Forecast on Election Night with Roriginal ↗
8 Nov 2016
Recently in the sister blog and elsewhereoriginal ↗
8 Nov 2016
“Another terrible plot”original ↗
8 Nov 2016
What is the chance that your vote will decide the election? Ask Stan!original ↗
7 Nov 2016
They say it because it’s true . . .original ↗
7 Nov 2016
Different election forecasts not so differentoriginal ↗
6 Nov 2016
Why I prefer 50% rather than 95% intervalsoriginal ↗
5 Nov 2016
Modeling statewide presidential election votes through 2028original ↗
4 Nov 2016
Kahan: “On the Sources of Ordinary Science Knowledge and Ignorance”original ↗
4 Nov 2016
Michael Betancourt has made NUTS even more awesome and efficient!original ↗
3 Nov 2016
How to improve science reporting? Dan Vergano sez: It’s not about reality, it’s all about a salaryoriginal ↗
3 Nov 2016
Science and personal decision makingoriginal ↗
3 Nov 2016
“Trump-Clinton Probably Won’t Be a Landslide. The Economy Says So.”original ↗
2 Nov 2016
Some modeling and computational ideas to look intooriginal ↗
2 Nov 2016
Postdoc opportunities with Jingchen Liu and Zhiliang Ying in Columbia statistics deptoriginal ↗
1 Nov 2016
Rotten all the way throughoriginal ↗
1 Nov 2016
3 secrets to giving a remote lecture!original ↗
1 Nov 2016
Time-Sharing Experiments for the Social Sciencesoriginal ↗
1 Nov 2016
Unintentional parody of Psychological Science-style research redeemed by Dan Kahan insightoriginal ↗
31 Oct 2016
“It’s not reproducible if it only runs on your laptop”: Jon Zelner’s tips for a reproducible workflow in R and Stanoriginal ↗
30 Oct 2016
Updating fast and sloworiginal ↗
29 Oct 2016
No evidence shark attacks swing electionsoriginal ↗
29 Oct 2016
Conflicts of interestoriginal ↗
29 Oct 2016
“The Warriors suck”: A Bayesian explorationoriginal ↗
29 Oct 2016
Happiness of liberals and conservatives in different countriesoriginal ↗
28 Oct 2016
“Generic and consistent confidence and credible regions”original ↗
27 Oct 2016
Yes, despite what you may have heard, you can easily fit hierarchical mixture models in Stanoriginal ↗
26 Oct 2016
The problems are everywhere, once you know to lookoriginal ↗
26 Oct 2016
Colorless green ideas tweet furiouslyoriginal ↗
26 Oct 2016
How not to analyze noisy data: A case studyoriginal ↗
25 Oct 2016
Ptolemaic inferenceoriginal ↗
24 Oct 2016
Spinoriginal ↗
24 Oct 2016
“How One Study Produced a Bunch of Untrue Headlines About Tattoos Strengthening Your Immune System”original ↗
23 Oct 2016
Another failed replication of power poseoriginal ↗
22 Oct 2016
Practical Bayesian model evaluation in Stan and rstanarm using leave-one-out cross-validationoriginal ↗
21 Oct 2016
Authors of AJPS paper find that the signs on their coefficients were reversed. But they don’t care: in their words, “None of our papers actually give a damn about whether it’s plus or minus.” All right, then!original ↗
21 Oct 2016
Avoiding model selection in Bayesian social researchoriginal ↗
21 Oct 2016
Pollsoriginal ↗
20 Oct 2016
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