Given observed data, perform a Kolmogorov-Smirnov type test comparing the cumulative distribution function of the concomitant covariate, defined as \(X \mid Y > t\), with \(t\) being the threshold, against the cumulative distribution function of the random vector of covariate.
schedastic.test(data, k, M = 1000L, xg, ng, bayes = TRUE, C = 5L, alpha = 0.05)a list with components
Delta maximum observed distance between the empirical distribution functions of the concomitant and complete covariate
DeltaM vector of length M containing the sample of maximum distances between the empirical distribution function of the concomitant complete covariate
critical double, critical value for the test statistic, computed as the \((1-alpha)\) level empirical quantile of DeltaM
pval double, p-value
design matrix of dimension n by 2 containing the complete data for the dependent variable (first column) and covariate (second column) in [0,1]
integer, number of exceedances for the generalized Pareto
integer, number of samples to draw from the posterior distrinution of the law of the concomitant covariate. Default: 1000
vector of covariate grid of dimension ng by 1 containing a sequence between zero and the last value of the corresponding covariate
length of covariate grid
logical indicating the bootstrap method. If FALSE, a frequentist bootstrap on the empirical cumulative distribution function of the concomitant covariate is performed. Default to TRUE
integer, hypermparameter entering the posterior distributyion of the law of the concomitant covariate. Default: 5
double, significance level for the critical value of the test, computed as the \((1-alpha)\) level empirical quantile of the sample of distances between the empirical cumulative distribution function of the concomitant and complete covariate. Default: 0.05