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Archive of posts filed under the Decision Theory category.

Estimating a customer satisfaction regression, asking only a subset of predictors for each person

Someone writes in with an interesting question: I’d like to speak with you briefly to get your thoughts on the imputation of missing data in a new online web-survey technique I’m developing. Our survey uses Split Questionnaire Design. The total number of surveys will vary in length with different customers, but will generally be between […]

Smullyan and the Randomistas

Steve Ziliak wrote in: I thought you might be interested in the following exchanges on randomized trials: Here are a few exchanges on the economics and ethics of randomized controlled trials, reacting to my [Zilliak's] study with Edward R. Teather-Posadas, “The Unprincipled Randomization Principle in Economics and Medicine”. Our study is forthcoming in the Oxford […]

On deck this week

Mon: Smullyan and the Randomistas Tues: Too Linear To Be True: The curious case of Jens Forster Wed: More on those randomistas Thurs: Estimating a customer satisfaction regression, asking only a subset of predictors for each person Fri: Quantifying luck vs. skill in sports Sat, Sun: Hey, it’s summer—time to take the weekends off. Have […]

Combining forecasts: Evidence on the relative accuracy of the simple average and Bayesian model averaging for predicting social science problems

Andreas Graefe sends along this paper (with Helmut Kuchenhoff, Veronika Stierle, and Bernhard Riedl) and writes: We summarize prior evidence from the field of economic forecasting and find that the simple average was more accurate than Bayesian model averaging in three of four studies; on average, the error of BMA was 6% higher than the […]

Average predictive comparisons in R: David Chudzicki writes a package!

Here it is: An R Package for Understanding Arbitrary Complex Models As complex models become widely used, it’s more important than ever to have ways of understanding them. Even when a model is built primarily for prediction (rather than primarily as an aid to understanding), we still need to know what it’s telling us. For […]

On deck this week

And now here’s something we hope you’ll really like (as Chris Hedges might say). A whole week of posts on statistics! Mon: My answer: Write a little program to simulate it Tues: Average predictive comparisons in R: David Chudzicki writes a package! Wed: Judicious Bayesian Analysis to Get Frequentist Confidence Intervals Thurs: Combining forecasts: Evidence […]

Comparing the full model to the partial model

Pat Lawlor writes: We are writing with a question about model comparison and fitting. We work in a group at Northwestern that does neural data analysis and modeling, and often would like to compare full models (e.g. neurons care about movement and vision) with various partial models (e.g. they only care about movement). We often […]

He’s not so great in math but wants to do statistics and machine learning

I received the following email from someone who wishes to remain anonymous: I am a longtime reader of your blog and it, along with other factors that I will explain briefly, has motivated to pursue a second masters degree in statistics and machine learning. The problem is, my math isn’t great. I understand statistics and […]

On deck this week

Mon: I hate polynomials Tues: Spring forward, fall back, drop dead? Wed: Bayes in the research conversation Thurs: The health policy innovation center: how best to move from pilot studies to large-scale practice? Fri: Stroopy names Sat: He’s not so great in math but wants to do statistics and machine learning Sun: Comparing the full […]

All the Assumptions That Are My Life

Statisticians take tours in other people’s data. All methods of statistical inference rest on statistical models. Experiments typically have problems with compliance, measurement error, generalizability to the real world, and representativeness of the sample. Surveys typically have problems of undercoverage, nonresponse, and measurement error. Real surveys are done to learn about the general population. But […]