Today, we will go further on the inference of copula functions. Some codes (and references) can be found on a previous post, on nonparametric estimators of copula densities (among other related things).  Consider (as before) the loss-ALAE data... [Read more...]

As mentioned in the course last week Venter (2003) suggested nice functions to illustrate tail dependence (see also some slides used in Berlin a few years ago). Joe (1990)'s lambda Joe (1990) suggested a (strong) tail dependence index. For lower t... [Read more...]

With Ewen (aka @3wen), not only we have been playing on Twitter this month, we have also been working on kernel estimation for densities of spatial processes. Actually, it is only a part of what he was working on, but that part on kernel estimation... [Read more...]

First, let us recall a standard result from linear algebra: "real symmetric matrices are diagonalizable by orthogonal matrices". Thus, any variance-covariance matrix can be written since a variance-covariance matrix is also definite positive. In ... [Read more...]

[This article was first published on Freakonometrics - Tag - R-english, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want [Read more...]

[This article was first published on Freakonometrics - Tag - R-english, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want [Read more...]

Friday in the course of statistics, we started the section on confidence interval, and like always, I got a bit confused with the degrees of freedom of the Student (should it be or ?) and which empirical variance (should we consider the one wher... [Read more...]

Last week, during the workshop on Statistical Methods for Meteorology and Climate Change (here), I discovered how powerful bayesian techniques could be, and that there were more and more bayesian statisticians. So, if I was to fully understand app... [Read more...]

cumsum ( rnorm(50), lend="butt", lwd=12, type="h" ) Cumulative sum of 50 draws from a normal distribution. File this under mysteries of the Central Limit Theorem. [Read more...]

More playing around with R. To create the graph above, I sampled 100 times from two different normal distributions, then plotted the ratio of times that the first distribution beat the second one on the y-axis. The second distribution always had a mean of 0, the mean of first distribution went from 0 ... [Read more...]