| bgeva-package {bgeva} | R Documentation |
Binary Generalized Extreme Value Additive Modelling
Description
bgeva provides a function for univariate modelling for binary rare events data with linear and nonlinear predictor effects when using the quantile function of the Generalized Extreme Value random variable.
Details
bgeva provides a function for flexible regression models for binary rare events data. The underlying representation and
estimation of the model is based on a penalized regression spline approach, with automatic smoothness selection. The
numerical routine carries out function minimization using a trust region algorithm from the package trust in combination with
an adaptation of a low level smoothness selection fitting procedure from the package mgcv.
bgeva supports the use of many smoothers as extracted from mgcv. Scale invariant tensor product smooths
are not currently supported. Estimation is by penalized maximum likelihood with automatic smoothness selection achieved
by using the approximate Un-Biased Risk Estimator (UBRE).
Confidence intervals for smooth components are derived using a Bayesian approach. Approximate p-values for testing
individual smooth terms for equality to the zero function are also provided. Functions plot.bgeva and
summary.bgeva extract such information from a fitted bgevaObject. Variable
selection is also possible via the use of shrinakge smoothers or information criteria.
Consider also using the faster and more stable version implemented in the gamlss() function of the
SemiParBIVProbit package. gamlss() also allows for a much wider choice of smoothers.
Author(s)
Raffaella Calabrese (University of Milano-Bicocca, Department of Statistics and Quantitative Methods), Giampiero Marra (University College London, Department of Statistical Science) and Silvia Osmetti (University Cattolica del Sacro Cuore, Department of Statistics)
Maintainer: Giampiero Marra giampiero.marra@ucl.ac.uk
References
Calabrese R., Marra G., Osmetti S.A. (2016), Bankruptcy Prediction of Small and Medium Enterprises Using a Flexible Binary Generalized Extreme Value Model. Journal of the Operational Research Society, 67(4), 604-615.