Goodness-of-fit tests using characterizations of continuous distributions

An Observation about Goodness-of-fit Tests of Distributions

We consider univariate goodness-of-fit tests. A result stating the non-existence of a uniformly most powerful test, for all combinations of null and alternative distributions, which are completely specified, is given.

New Goodness-of-fit Tests for Pareto Distributions*

A new approach to goodness-of-fit for Pareto distributions is introduced. Based on Euclidean distances between sample elements, the family of statistics and tests is indexed by an exponent in (0,2) on Euclidean distance. The corresponding tests are statistically consistent and have excellent performance when applied to heavy-tailed distributions. The exponent can be tailored to the particular P...

Nonparametric Goodness-of-Fit Tests for Discrete Null Distributions

Methodology extending nonparametric goodness-of-fit tests to discrete null distributions has existed for several decades. However, modern statistical software has generally failed to provide this methodology to users. We offer a revision of R’s ks.test() function and a new cvm.test() function that fill this need in the R language for two of the most popular nonparametric goodness-of-fit tests. ...

The Comparison Between Goodness of Fit Tests for Copula

‎Copula functions as a model can show the relationship between variables‎. ‎Appropriate copula function for a specific application is a function that shows the dependency between data in a best way‎. ‎Goodness of fit tests theoretically are the best way in selection of copula function‎. ‎Different ways of goodness of fit for copula exist‎. ‎In this paper we will examine the goodness of fit test...

On Goodness-of-fit Tests