Consider our loss-ALAE dataset, and - as in Frees & Valdez (1998) - let us fit a parametric model, in order to price a reinsurance treaty. The dataset is the following, > library(evd) > data(lossalae) > Z=lossalae > X=Z;Y=Z ...
Posts Tagged ‘ likelihood ’
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) > ...
MAT8886 a short word on profile likelihood
Profile likelihood is an interesting theory to visualize and compute confidence interval for estimators (see e.g. Venzon & Moolgavkar (1988)). As we will use is, we will plot But more generally, it is possible to consider where . Then (...
Hey! I made you some Wiener processes!
Check them out. Here are thirty homoskedastic ones: > homo.wiener for (j in 1:30) { for (i in 2:length(homo.wiener)) { homo.wiener for (j in 1:30) { plot( homo.wiener, type = "l", col = rgb(.1,....
The truncated Poisson
A common model for counts data is the Poisson. There are cases however that we only record positive counts, ie there is a truncation of 0. This is the truncated Poisson model. To study this model we only need the total counts and the sample size. This comes from the sufficient statistic principle as the
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
