For the last decade or so, the go-to software for Bayesian statisticians has been BUGS (and later the open-source incarnation, OpenBugs, or JAGS). BUGS is used for multi-level modeling: using a specialized notation, you can define random variables of various distributions, set Bayesian priors for their parameters, and create the ... [Read more...]

Here is the list  of courses I wish to teach next year at Chiang Mai School of Economics, not so sure about the demand there! Undergraduate (B.Econ.) ECON 304: Economics Statistics (with R) ECON 408: Research Design in Economics ECON 417: Managerial Economics ECON 419: Economic Theory and Entrepreneurship ECON 443: Industrial Economics ECON 4... [Read more...]

After a sunny weekend to unpack and unwind, I am now back to my normal schedule, on my way to Paris-Dauphine for an R (second-chance) exam. Except for confusing my turn signal for my wiper, thanks to two weeks of intensive driving in four Australian states!, things are thus back ... [Read more...]

A National Institute for Occupational Safety and Health study, published in March, found that professional American football (NFL) players lived longer, on average, than similar "mere mortals" in the general population. Football is a dangerous sport, so that might seem surprising at first, until you consider the fact that NFL ... [Read more...]

Why trust some supposed laws of statistical sampling and convergence when you can just test them yourself? If you have a computer with R installed (also recommended: Rstudio) then you can stop dithering about whether these n=1000 studies cited in the n... [Read more...]

Recently a few members of R Core have indicated that part of what slows down the development of R as a language is that it has become increasingly difficult over the years to achieve consensus among the core developers of the language. Inspired by these claims, I decided to look ... [Read more...]

I’ve been interviewed twice in the last year: For DecisionStats, 9 August 2012. For Data Mining Research, 21 October 2011. Republished in Amstat News, 1 December 2011. Some readers of this blog might find them interesting. I said a few things in t... [Read more...]

Despite my preference for SAS over R, there are some add-ons to “basic” R that I’ve found that have made my learning process way easier.  While I’m still in my infancy in learning R, I feel like once I found … Continue reading →Getting Started Using R, Part 1: RStudio ... [Read more...]

Forecasting sporting events is a growing research area. The International Journal of Forecasting even had a special issue on sports forecasting a couple of years ago. The London 2012 Olympics has attracted a few forecasters trying to predict medal counts, world records, etc. Here are some of the articles I’ve ... [Read more...]

I’m happy to announce that I’ve started writing a new book for O’Reilly, which will focus on teaching readers how to use Multi-Armed Bandit Algorithms to build better websites. My hope is that the book can help web developers build up an intuition for the core conundrum ... [Read more...]

For the past 18 months, it seems like all I’ve heard about in the digital marketing industry is “big data”, and with that, mentions of using Hadoop and R to solve these sorts of problems.  Why are these tools the … Continue reading →Learning R has really made me appreciate SAS ... [Read more...]

One of the shortcomings of regression (both linear and logistic) is that it doesn’t handle categorical variables with a very large number of possible values (for example, postal codes). You can get around this, of course, by going to another modeling technique, such as Naive Bayes; however, you lose ... [Read more...]

At MSR this week, we had two very good talks on algorithmic methods for tuning the hyperparameters of machine learning models. Selecting appropriate settings for hyperparameters is a constant problem in machine learning, which is somewhat surprising given how much expertise the machine learning community has in optimization theory. I ... [Read more...]

Here are the slides for the second day of my course at Monash University, Melbourne, in the Special Lectures in Econometrics, with a strong strong similarity with the slides of my course in Roma this Spring. (Ah, sunny Roma…) The first day lecture was very well attended and I hope ... [Read more...]

Here are the slides for the first day of my course at Monash University, Melbourne, in the Special Lectures in Econometrics, with a strong similarity with the slides of my course in Wharton, two years ago. (Be sure to check slide 67! If the update on slideshare works from my flat ... [Read more...]

Introduction In the third installment of my series of criticisms of NHST, I focused on the notion that a p-value is nothing more than a one-dimensional representation of a two-dimensional space in which (1) the measured size of an effect and (2) the precision of this measurement have been combined in such ... [Read more...]

Lets go one with the second part of learning R by doing R (you will find the first part here. As we have used vectors, matrices and loops in the first part, we will concentrate on graphics in this one. but first we will need data to plot: Sometimes you ... [Read more...]

[This article was first published on Xi'an's Og » R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your [Read more...]

Over the last few weeks, I’ve made a concerted effort to develop a basic suite of optimization algorithms for Julia so that Matlab programmers used to using fminunc() and R programmers used to using optim() can start to transition code over to Julia that requires access to simple optimization ... [Read more...]

Geography is often about statistics as it is the basis for fast exchange of information: providing a mean and standard deviation to the audience is often much easier then showing raw data: Learning a script language for this purpose can be a hard-ass work. But I think it is more ... [Read more...]