Nonparametric statistics

Summary

Nonparametric statistics is the part of statistics that does not assume the law of the data belongs to a family described by finitely many parameters. It uses models of infinite dimension, such as all continuous distributions on $\R$ or all smooth densities. It also includes distribution-free methods, whose behaviour under the null hypothesis does not depend on the unknown law. Typical tools are the empirical distribution function, rank tests, kernel density estimation and the bootstrap.