Deliverance!!! We have at last completed our book! Bayesian Essentials with R is off my desk! In a final nitty-gritty day of compiling and recompiling the R package bayess and the LaTeX file, we have reached versions that were in par with our expectations. The package has been submitted to CRAN (it has gone back 
Collinearity, or excessive correlation among explanatory variables, can complicate or prevent the identification of an optimal set of explanatory variables for a statistical model. For example, forward or backward selection of variables could produce inconsistent results, variance partitioning analyses may be unable to identify unique sources of variation, or parameter estimates may include substantial amounts 
(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...
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...
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 
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 →
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,...
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 
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 →
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...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
