In which we make incremental improvements to our code for planning by incremental improvements. Assignment, code. Introduction to Statistical Computing

In which we make incremental improvements to our code for planning by incremental improvements. Assignment, code. Introduction to Statistical Computing

I’m extremely grateful to Drs. Owhadi, Scovel and Sullivan for replying to my request for “a plain Jane” explication of their interesting paper, “When Bayesian Inference Shatters”, and especially for permission to post it. If readers want to ponder the paper awhile and send me comments for guest posts or “U-PHILS*” (by OCT 15), let […]

Continuing from random coefficients part 1, it is time for the second part. To quote the SAS/STAT manual 'a random coefficients model with error terms that follow a nested structure'. The additional random variable is monthc, which is a factor con...

From 1982: The necessary conceit of the essayist must be that in writing down what is obvious to him he is not wasting his reader’s time. The value of what he does will depend on the quality of his perception, not on the length of his manuscript. Too many dull books about literature would have […]The post On blogging appeared first on Statistical Modeling, Causal Inference, and Social Science.

Quite frequently, someone on the internet discovers the Monty Hall paradox, and become so enthusiastic that it becomes urgent to publish an article – or a post – about it. The latest example can be http://www.bbc.co.uk/news/magazine-24045598. I won’t blame them, I did the same a few years ago (see http://freakonometrics.hypotheses.org/776, or http://freakonometrics.hypotheses.org/775, in French). My point today is that the Monty Hall paradox raise an important question, about information. How comes…

Editor's Note: This post was written by Brian Caffo, occasional Simply Statistics contributor and Director of Graduate Studies in the Department of Biostatistics at Johns Hopkins. This was written primarily for incoming graduate students, but if you're planning on moving … Continue reading →

This post is by Phil Price. The New York Times recently ran an article entitled “How Exercise Can Help Us Eat Less,” which begins with this: “Strenuous exercise seems to dull the urge to eat afterward better than gentler workouts, several new studies show, adding to a growing body of science suggesting that intense exercise […]The post You heard it here first: Intense exercise can suppress appetite appeared first on…

Psychology researcher Chris Chabris writes: Rolf Dobelli, a Swiss writer, published a book called The Art of Thinking Clearly earlier this year with HarperCollins in the U.S. The book’s original German edition was a #1 bestseller, and the book has sold over one million copies worldwide. In perusing Mr. Dobelli’s book, we noticed several familiar-sounding […]The post Swiss Jonah Lehrer appeared first on Statistical Modeling, Causal Inference, and Social Science.

A reposted item of news about MCMSki IV: as posted by Brad Carlin this afternoon to the Biometrics Section and Bayesian Statistical Science Section of the ASA, The fifth joint international meeting of the IMS (Institute of Mathematical Statistics) and ISBA (International Society for Bayesian Analysis), nicknamed “MCMSki IV”, will be held in Chamonix Mont-Blanc, […]

This week I read an interesting blog post that led to a discussion about specifying the frequencies of observations in a regression model. In SAS software, many of the analysis procedures contain a FREQ statement for specifying frequencies and a WEIGHT statement for specifying weights in a weighted regression. Theis [...]

Over the past few months, we’ve talked about modeling with particle physicists (Allen Caldwell), astrophysicists (David Hogg, who regularly comments here), and climate and energy usage modelers (Phil Price, who regularly posts here). Big Science Black Boxes We’ve gotten pretty much the same story from all of them: their models involve “big science” components that […]The post Samplers for Big Science: emcee and BAT appeared first on Statistical Modeling, Causal…

Didier Ruedin writes: Here’s something I’ve been wondering for a while, and I thought your blog might be the right place to get the views from a wider group, too. How would you describe that feeling when—after going through the theory, collecting data, specifying the model, perhaps debugging the code—you hit enter and get the […]The post Do you ever have that I-just-fit-a-model feeling? appeared first on Statistical Modeling, Causal…

Taking a break from Statistical Mechanics I noticed Corey Yanofsky, whom I respect a great deal, is starting a blog. Corey plans to explore Dr. Mayo’s Severity Principle, which he describes as the “strongest defense of frequentism I’ve ev...

To promote research and education in statistical genetics and genomics, some of us in the community would like to establish a statistical genetics and genomics section of the American Statistical Association (ASA). Having an ASA section gives us certain advantages, … Continue reading →

Suite du cours ACT2121, de préparation pour l’examen P de la SOA (probability). Un nouveau jeu d’exercices, sur les thèmes 4-5 (tel que classifié dans le livre de Jacques Labelle, qui servira de référence pour ce cours) Formule de la probabilité totale, et formule de Bayes, #4, et lois discrètes #5 ACT2121-A2013-45.pdf On fera des exercices sur la loi de Poisson la semaine prochaine, et l’intra du 27 septembre portera sur les…

Don’t be so quick to place politicians’ views of “national interests” above the mood of the publicMore on those pollsters who apparently throw away completed survey responsesA theory of the importance of Very Serious People in the Democratic Pa...

Professor Andrew Gelman is a pioneer in statistics blogging. His blog is one of my regular reads, a mixture of theoretical pieces, applied work, psychological musing, rants about unethical academics, advocacy of statistical graphics, and commentary on literature. He's one of the few statisticians who gets opinion pieces published in the New York Times. His expertise is statistics in politics, but I also enjoy his work on the stop-and-frisk policies…

This morning, in the ACT2040 class (on non-life insurance), we’ve discussed the difference between observable and non-observable heterogeneity in ratemaking (from an economic perspective). To illustrate that point (we will spend more time, later on, discussing observable and non-observable risk factors), we looked at the following simple example. Let denote the height of a person. Consider the following dataset > Davis=read.table( + "http://socserv.socsci.mcmaster.ca/jfox/Books/Applied-Regression-2E/datasets/Davis.txt") There is a small typo in the dataset,…