Goodness-of-Fit Tests via Kernel Density Estimation: Univariate and Multivariate Approaches
This paper introduces goodness-of-fit tests based on leave-one-out kernel density estimation (KDE) for both univariate and multivariate data. The proposed test statistic takes the form of a log-likelihood ratio comparing a parametric null model with a nonparametric KDE-based estimator, where the leave-one-out construct...