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 ‘ mle ’
Montreal R Workshop: Likelihood Methods and Model Selection
Monday, March 19, 2012 14h-16h, Stewart Biology N4/17 Corey Chivers, McGill University Department of Biology This workshop will introduce participants to the likelihood principal and its utility in statistical inference. By learning how to formalize models through their likelihood function, participants will learn how to confront these models with data in order to make statistical 
bounded normal mean
A few days ago, one of my students, Jacopo Primavera (from La Sapienza, Roma) presented his “reading the classic” paper, namely the terrific bounded normal mean paper by my friends George Casella and Bill Strawderman (1981, Annals of Statistics). Even though I knew this paper quite well, having read (and studied) it myself many times, 
Multivariate probit regression using (direct) maximum likelihood estimators
Consider a random pair of binary responses, i.e. with taking values 1 or 2. Assume that probability can be function of some covariates . The Gaussian vector latent structure A standard model is based a latent Gaussian structure, i.e. there exi...
How many tanks? MC testing the GTP
It’s 1943 and you work for the good guys. A handful of German tanks have been captured, and each one has a serial number. This is back when serial numbers were still presumed to come in serial, one right after the other. Given your collection of numbered tanks, and assuming that any existing tank was
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
