In this article, we develop a test for the null hypothesis that a real-valued function belongs to a given parametric set against the non-parametric alternative that it is monotone, say decreasing. The method is described in a general model that covers the monotone density model, the monotone regression and the right-censoring model with monotone hazard rate. The criterion for testing is an L p-...

Copulas are often used in finance to characterize the dependence between assets. However, a choice of the functional form for the copula is an open question in the literature. This paper develops a goodness-of-fit test for copulas based on positive definite bilinear forms. The suggested test avoids the use of plug-in estimators that is the common practice in the literature. The test statistics ...

We propose a nonparametric statistical test for goodness-of-fit: given a set of samples, the test determines how likely it is that these were generated from a target density function. The measure of goodness-of-fit is a divergence constructed via Stein’s method using functions from a Reproducing Kernel Hilbert Space. Our test statistic is based on an empirical estimate of this divergence, takin...

Introduction. The goodness of t tests of the empirical data to one theoretical distribution, and particularly the χ of Pearson, are very used in practice, also as preliminary step for the use of methods of parametric inference which are consistent with precise models of distribution. Gasser [10] explored the χ test for correlated data by simulations and, subsequently, Moore [14] and Gleser and ...

Scientists often use the Chi-square (χ) test to determine the “goodness of fit” between theoretical and experimental data. In this test, we compare observed values with theoretical or expected values. Observed values are those that the researcher obtains empirically through direct observation. The theoretical or expected values are developed on the basis of an established theory or a working hy...