parametric bootstrapping in a generalized extreme value regression model for binary response: application in health study

Generalized extreme value regression is often more adapted when we investigate a relationship between a binary response variable that represents a rare event and potential predictors. in particular, we use the quantile function of the generalized extreme value distribution as the link function. bootstrapping assigns measures of accuracy (bias, variance, confidence intervals, prediction error, hypotheses testing) to sample estimates. this technique allows estimation of the sampling distribution of almost any statistic using random sampling methods. bootstrapping estimates the properties of an estimator by measuring those properties when sampling from an approximating distribution. in this paper, we fit the generalized extreme value regression model and perform a parametric bootstrap method for testing hypotheses and confidence interval estimation of parameters for the generalized extreme value regression model with a real data application.