A Review of Representative Points of Statistical Distributions and Their Applications

Statistical modeling relies on a diverse range of statistical distributions, encompassing both univariate and multivariate distributions and/or discrete continuous distributions. In the literature, numerous methods have been proposed to approximate The most commonly used approach is use empirical distribution which obtained from random sample drawn distribution. However, it very likely that suffers an accuracy problem when underlying distribution, especially if size not sufficient. order improve inferences, various alternative forms approximation were in literature. choice support points for approximation, known as Representative Points (RPs), becomes extremely important terms approximations. this paper we give review three main constructing RPs, namely based Monte Carlo method, number-theoretic method (or quasi-Monte method), mean square error aiming introduce such or mathematical community. Additional approaches forming RPs are also briefly discussed. focuses certain critical aspects theoretical properties computational algorithms RPs. We address issue application through studying practical problems provide evidence RPs’ advantages over samples approximating