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Recent items include:

  • Survey Statistics: equivalent models, equivalent weights (locally)
  • Survey Statistics: Modeling Complex Contingency Tables
  • Survey Statistics: quantifying uncertainty in ranked choice voting polls

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statmodeling.stat.columbia.edu /1 month ago

Survey Statistics: equivalent models, equivalent weights (locally)

Last month we saw that the Times/Siena Poll is now using energy balancing weights (Huling & Mak, 2024). In a toy example, we saw under which outcome models these weighting meth...

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statmodeling.stat.columbia.edu /3 weeks ago

Survey Statistics: Modeling Complex Contingency Tables

Andrew looped me into an email thread with folks working on poststratification with partial population information. (He knew I’d be interested, see “poststratification without popu...

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statmodeling.stat.columbia.edu /1 month ago

Survey Statistics: quantifying uncertainty in ranked choice voting polls

We’ve talked about uncertainty in polls (see Margin of Error, Total Margin of Error, Total Margin of Error II) and we’ve talked about ranked data (see exploded logit !). A new pape...

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statmodeling.stat.columbia.edu /2 weeks ago

Survey Statistics: more on SynthMargins and Bayes-Raking

Last week we discussed SynthMargins, a method from the poster Modeling Complex Contingency Tables that uses partial information (margins) about poststratification variables. On the...

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statmodeling.stat.columbia.edu /1 week ago

Survey Statistics: logit shift and raking

We’ve been discussing how to use population margin information about poststratification variables (see Modeling Complex Contingency Tables and SynthMargins & Bayes-Raking). I w...

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statmodeling.stat.columbia.edu /1 month ago

Survey Statistics: poststratification without population level information

Poststratification uses population data on X to estimate E(Y) via E(E(Y | X, R = 1)), where R = 1 are survey respondents who provide Y and X. When the inner expectation “E” is esti...

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statmodeling.stat.columbia.edu /1 month ago

Survey Statistics: structured MRP to smooth survey weights

Last week, Raphael K shared a concern: adjusting for lots of variables can lead to very large weights. So today let’s dive into Si et al. 2020, who saw this in constructing survey...

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statmodeling.stat.columbia.edu /1 month ago

“Placebo tests deserve a model, not just a glance.”

Miha Gazvoda shares this post with the above title and the subtitle, “Using Bayesian multilevel models to correct bias and calibrate uncertainty.” He’s using the chickens model fro...

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towardsdatascience.com /1 month ago

Building Models in Two Worlds: From Latent Constructs to Behavioral Signals

My PhD models tried to explain why people engage. My industry models predict who will. The statistics barely changed. Everything around them did. The post Building Models in Two Wo...

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statmodeling.stat.columbia.edu /4 weeks ago

Survey Statistics: wanting workflow

Last week Andrew commented that we need a more transparent workflow for survey statistics. So I looked in the new Bayesian Workflow book: Chapter 19 “Building up to a hierarchical...

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statmodeling.stat.columbia.edu /1 month ago

He fit the same statistical models with three different software and got much different estimates. It’s another dimensi...

Scott Cunningham writes: You’ll appreciate this I think. I ran Claude code on 96 specs for a popular difference-in-difference estimator with the identical specifications, ranging c...

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statmodeling.stat.columbia.edu /1 month ago

“Making Statistics Work: Information Theory and Bayesian Inference”

I took a look at the above-titled book by economists Duncan Foley and Ellis Scharfenaker. It’s an interesting read, in many ways a throwback to the 1950s when a group of mathematic...

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statmodeling.stat.columbia.edu /1 month ago

How to avoid the “clean data, clean model” trap when teaching statistics and data science

Parthsarthi Joshi writes: There is a stark problem that I have observed with how data science is taught in most books and tutorials – the concepts are taught on an individual level...

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statmodeling.stat.columbia.edu /3 weeks ago

The improvement in political analysis in the past 25 years, as demonstrated by excellent demonstrations of statistical w...

As with baseball, football, and basketball (and I’m sure other sports too), the standard of political analytics is just so much higher than it was, decades ago. I was talking with...

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statmodeling.stat.columbia.edu /1 month ago

What do we learn from bestseller regressions?

Gaurav Sood writes: I was reading ‘The Bestseller Code.’ The book reports results from some regressions of the form: bestseller or not ~ features of content This got me thinking ab...

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towardsdatascience.com /1 month ago

Why Your Best Predictive Model Gives the Wrong Treatment Effect

Why prediction-driven variable selection misses confounders and how Bayesian Adjustment for Confounding attempts to fix it. The post Why Your Best Predictive Model Gives the Wrong...

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statmodeling.stat.columbia.edu /1 month ago

It’s all about the nonlinearity: An interesting statistical example of flaws in a voter impact index

The following came in the email the other day: I’m reaching out to introduce the Voter Impact Index, a new data tool from PowerMoves that assigns every U.S. zip code a voter impact...

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suparnachowdhury.medium.com /1 month ago

Statistics for Data Scientists (Part 2): Finding Hidden Patterns in Your Data

Learn how distributions, relationships, and class imbalance reveal insights.Continue reading on Medium »

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statmodeling.stat.columbia.edu /1 month ago

Posterior predictive checking is for non-Bayesians too!

When I first started working on posterior predictive checking back in 1988, it was as a device for determining equivalent degrees of freedom for a chi-squared test for a model with...

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statmodeling.stat.columbia.edu /1 week ago

“Why did ANOVA fall out of fashion?”

A student asks the above question. My response: Anova is still important; it’s just been subsumed by hierarchical models. The link is to my 2005 paper, Analysis of variance: Why it...

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r-bloggers.com /1 month ago

Evaluating LLMs/AI for Media Planning in R

LLMs can produce a convincing media recommendation in a few seconds. The more useful question is whether the recommendation is correct: are the reach calculations right, are the as...

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statmodeling.stat.columbia.edu /3 weeks ago

Frequentism for Bayesians: He wants to teach frequentist methods to engineering students with a strong Bayesian backgro...

Beyond the teaching question, this is an interesting topic on its own: thinking about classical statistical ideas of point estimation, hypothesis testing, and uncertainty quantific...

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thepredicto.medium.com /1 week ago

Predicto Atlas: One Model to Forecast Them All

A single deep learning model trained across 528 stocks, built to forecast volatility.Continue reading on Medium »

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medium.com /2 weeks ago

The Dhahran Probability Density Function: Rethinking Parameter Estimation Beyond Gaussian…

What if the probability distribution underlying your parameter estimation algorithm is the reason you’re getting the wrong answer?Continue reading on Medium »

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statmodeling.stat.columbia.edu

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towardsdatascience.com

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r-bloggers.com

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