An interview with Felix Salmon by Kaiser Fung

I'm sadly not able to attend the Joint Statistical Meetings this year (where Nate Silver is the keynote speaker!) in the great city of Montreal. I'm looking forward to checking out the chatter on #JSM2013 but in the meantime, here are … Continue reading →

I've been working on a package called [crayon-541a263d34d32972269363-i/] that helps create nice-looking plots in R and it is now up on CRAN. You can get it by typing [crayon-541a263d34d5f643444330/] directly into the command line in R. See how it works by typing [crayon-541a263d34d67995324632-i/] or reading the details here. Below I describe the basic structure and functions. […]

This article describes how to generate random samples from the multinomial distribution in SAS. The content is taken from Chapter 8 of my book Simulating Data with SAS. The multinomial distribution is a discrete multivariate distribution. Suppose there are k different types of items in a box, such as a [...]

We often use AIC to discern the best model among candidates. Now suppose we have two (non-parametric) models, which use mass points and weights to model a random variable: model A uses 4 mass points to model a random variable (i.e. the height of men in Singapore) model B uses 5 mass points to mode […]

Symposium magazine (“Where Academia Meets Public Life”) has some fun stuff this month: Learning to Read All Over Again Lutz Koepnick What produces better students – reading in print or reading on-line? The answer is both. The Elusive Quest for Research Innovation Claude S. Fischer Much of what is considered “new research” has actually been […]The post New issue of Symposium magazine appeared first on Statistical Modeling, Causal Inference, and…

The $4 million teacher. I love the idea that teaching is becoming a competitive industry where the best will get the kind of pay they really really deserve. I can't think of another profession where the ratio of (if you … Continue reading →

Histogram: LM estimates of Intercepts Histogram: LM estimates of Gradient QQ Plot: LM estimates of Intercepts QQ Plot: LM estimates of Gradient Figure 1: Gradient appears to follow a normal distribution more than intercept . When do we use a parametric model, and when do we use a non-parametric one? In […]

Andy Cooper writes: A link to an article, “Four Assumptions Of Multiple Regression That Researchers Should Always Test”, has been making the rounds on Twitter. Their first rule is “Variables are Normally distributed.” And they seem to be talking about the independent variables – but then later bring in tests on the residuals (while admitting […]The post What are the key assumptions of linear regression? appeared first on Statistical Modeling,…

David Hsu writes: I have a (perhaps) simple question about uncertainty in parameter estimates using multilevel models — what is an appropriate threshold for measure parameter uncertainty in a multilevel model? The reason why I ask is that I set out to do a crossed two-way model with two varying intercepts, similar to your flight […]The post Uncertainty in parameter estimates using multilevel models appeared first on Statistical Modeling, Causal…

This is a study of breastfeeding and its impact on IQ that has been making the rounds on a number of different media outlets. I first saw it on the Wall Street Journal where I was immediately drawn to this … Continue reading →

X marks the spot. I’ll post the slides soon (not just for the students in my class; these should be helpful for anyone teaching Bayesian data analysis from our book). But I don’t think you’ll get much from reading the slides alone; you’ll get more out of the book (or, of course, from taking the […]The post My course this fall on l’analyse bayésienne de données appeared first on Statistical…

Christian Robert’s reply grows out of my last blogpost. On Xi’an’s Og : A quick reply from my own Elba, in the Dolomiti: your arguments (about the sad consequences of the SLP) are not convincing wrt the derivation of SLP=WCP+SP. If I built a procedure that reports (E1,x*) whenever I observe (E1,x*) or (E2,y*), this obeys […]

I recently came across a very interesting paper by Y. Yu and X. Meng[1] who present an interweaving strategy between different model parameterizations to improve mixing. It is well known that different model parameterizations can perform better than others under certain conditions. Papaspiliopoulos, Roberts and Sköld [2] present a general framework for how to parameterize […] The post Model Scale Parameterization for MCMC Efficiency appeared first on Lindons Log.