(see John Wilkins’ article on the – interesting – history of that phrase http://scienceblogs.com/evolvingthoughts/…). We will see, this week in class, several smoothing techniques, for insurance ratemaking. As a starting point, assume that we do not want to use segmentation techniques: everyone will pay exactly the same price. no segmentation of the premium And that price should be related to...
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Tables from R into Word
A good looking table matters!
This tutorial is on how to create a neat table in Word by combining knitr and R Markdown. I'll be using my own function, htmlTable, from the Gmisc package.
Background: Because most journals that I submit to want...
Proposed techniques for communicating the amount of information contained in a statistical result
A couple of weeks ago, I posted about how much we can expect to learn about the state of the world on the basis of a statistical significance test. One way of framing this question is: if we’re trying to come to scientific conclusions on the basis of statistical results, how much can we update 
Generating Labels for Supervised Text Classification using CAT and R
The explosion in the availability of text has opened new opportunities to exploit text as data for research. As Justin Grimmer and Brandon Stewart discuss in the above paper, there are a number of approaches to reducing human text to … Continue reading →
A random walk ? What else ?
Consider the following time series, What does it look like ? I know, this is a stupid game, but I keep using it in my time series courses. It does look like a random walk, doesn’t it ? If we use Philipps-Perron test, yes, it does, > PP.test(x) Phillips-Perron Unit Root Test data: x Dickey-Fuller = -2.2421, Truncation lag parameter = 6,...
A slightly different introduction to R, part III
I think you’ve noticed by now that a normal interactive R session is quite messy. If you don’t believe me, try playing around for a while and then give the history() command, which will show you the commands you’ve typed. If you’re anything like me, a lot of them are malformed attempts that generated some 
On p-value
Albert Schweitzer said: “Example is not the main thing in influencing others. It is the only thing.”, so I start with it. Generate two random samples from Normal distribution. Test the hypothesis number one: , do not reject, then test … Continue reading →
Overdispersion with different exposures
In actuarial science, and insurance ratemaking, taking into account the exposure can be a nightmare (in datasets, some clients have been here for a few years – we call that exposure – while others have been here for a few months, or weeks). Somehow, simple results because more complicated to compute just because we have to take into account...
Bayesian model choice for the Poisson model
Following Arthur Charpentier‘s example, I am going to try to post occasionally on material covered during my courses, in the hope that it might be useful to my students, but also to others. In the second practical of the Bayesian Case Studies course, we looked at Bayesian model choice and basic Monte Carlo methods, looking 
Regression on categorical variables
This morning, Stéphane asked me tricky question about extracting coefficients from a regression with categorical explanatory variates. More precisely, he asked me if it was possible to store the coefficients in a nice table, with information on the variable and the modality (those two information being in two different columns). Here is some code I did to produce the...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
