This article provide a brief background about power and sample size analysis. Then, power and sample size analysis is computed for the Z test. Continue reading →

If you're reporting on the results of a statistical analysis for a journal or report, you'll probably be building a table comparing two or models. Such tables may include variables in the model, parameter estimates, and p-values, and model summary statistics. If you want to include such tables based on lm, glm, svyglm, gee, gam, polr, survreg or coxph...

Data can often be usefully conceptualized in terms affiliations between people (or other key data entities). It might be useful analyze common group membership, common purchasing decisions, or common patterns of behavior. This post introduces bipartite/affiliation network data and provides … Continue reading →

Editor’s note: R-bloggers does not take a political side. Since this is an important topic, this post has the comments turned on. Also, If you wish to write a reply post (which includes an R context), you are welcome to contact me to have it published. This post was written by Prof. H. D. Vinod. Fordham University, New York.

For the last course MAT8886 of this (long) winter session, on copulas (and extremes), we will discuss risk aggregation. The course will be mainly on the problem of bounding the distribution (or some risk measure, say the Value-at-Risk) for two random variables with given marginal distribution. For instance, we have two Gaussian risks. What could be be worst-case scenario...

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 The first step can be to estimate marginal distributions, independently. Here, we consider lognormal distributions for both components, > Fempx=function(x) mean(X<=x) >...