As mentioned in the course on copulas, a nice tool to describe dependence it Kendall's cumulative function. Given a random pair with distribution , define random variable . Then Kendall's cumulative function is Genest and Rivest (1993) intr...
Blog Archives
Association and concordance measures
Following the course, in order to define assocation measures (from Kruskal (1958)) or concordance measures (from Scarsini (1984)), define a concordance function as follows: let be a random pair with copula , and with copula . Then define the so-...
Unit root, or not ? is it a big deal ?
Consider a time series, generated using set.seed(1) E=rnorm(240) X=rep(NA,240) rho=0.8 X=0 for(t in 2:240){X=rho*X+E} The idea is to assume that an autoregressive model can be considered, but we don't know the value of the parameter. ...
That damn R-squared !
Another post about the R-squared coefficient, and about why, after some years teaching econometrics, I still hate when students ask questions about it. Usually, it starts with "I have a _____ R-squared... isn't it too low ?" Please, feel free to fi...
Inference and autoregressive processes
Consider a (stationary) autoregressive process, say of order 2, for some white noise with variance . Here is a code to generate such a process, > phi1=.5 > phi2=-.4 > sigma=1.5 > set.seed(1) > n=240 > WN=rnorm(n,sd=sigma) > ...
Border bias and weighted kernels
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...
Border bias and weighted kernels
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 has been the opportunity to write a short paper, that can now be downloaded on hal. The problem...
Visualizing uncertainty using Jackknife
Once again, I (re)discovered last week at the Rmetrics conference that old toolds can be extremely interesting to illustrate complex ideas, like uncertainty in fnancial markets, and stock prices. For instance a 99.5% quantile: we look for the scena...
Simple and heuristic optimization
This week, at the Rmetrics conference, there has been an interesting discussion about heuristic optimization. The starting point was simple: in complex optimization problems (here we mean with a lot of local maxima, for instance), we do not ne...
Simple and heuristic optimization
This week, at the Rmetrics conference, there has been an interesting discussion about heuristic optimization. The starting point was simple: in complex optimization problems (here we mean with a lot of local maxima, for instance), we do not necessarily need extremely advanced algorithms that do converge extremly fast, if we cannot ensure that they reach the optimum. Converging extremely fast, with a...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
