A primary problem data scientists face again and again is: how to properly adapt or treat variables so they are best possible components of a regression. Some analysts at this point delegate control to a shape choosing system like neural nets. I feel such a choice gives up far too ... [Read more...]

On http://www.bakadesuyo.com, there was recently an interesting discussion about infidelity, the key question being "at what ages are men and women most likely to have affairs?" The discussion is based on some graphs, e.g. The source is a paper b... [Read more...]

Statistical Methods for the Chain Ladder Technique Revisited: Source: Statistical Methods for the Chain Ladder Technique Demo Background Forecasting outstanding claims and setting up suitable reserves to meet these claims is an important part of the b... [Read more...]

As Mark Twain said "the art of prophecy is very difficult, especially about the future" (well, actually I am not sure Mark Twain was the  first one to say so, but if you're interested by that sentence, you can look here). I have been rather su... [Read more...]

Splines in regression is something which looks like a black box (or maybe like some dishes you get when you travel away from home: it tastes good, but you don't what's inside... even if you might have some clues, you never know for sure*). With spl... [Read more...]

We've seen in the previous post (here)  how important the *-cartesian product to model joint effected in the regression. Consider the case of two explanatory variates, one continuous (, the age of the driver) and one qualitative (, gasoline ve... [Read more...]