Fitting Semi-Parametric Generalized log-Gamma Regression Models
Description
Set of tools to fit a linear multiple or semi-parametric regression
models with the possibility of non-informative random right or left censoring.
Under this setup, the localization parameter of the response variable distribution is modeled by using linear multiple regression
or semi-parametric functions, whose non-parametric components may be approximated
by natural cubic spline or P-splines. The supported distribution for the model error is a generalized log-gamma distribution which includes
the generalized extreme value and standard normal distributions as important special cases. Inference is based on likelihood, penalized likelihood and bootstrap methods.
Lastly, some numerical and graphical devices for diagnostic of the fitted models are offered.