Goodness of Fit Tests Based on Kernel Density Estimators

Goodness of Fit Tests Based on Kernel Density Estimators

The paper is devoted to goodness of fit tests based on probability density estimates generated by kernel functions. The test statistic is considered in the form of maximum of the normalized deviation of the estimate from its expected value or a hypothesized distribution density function. A comparative Monte Carlo power study of the investigated criterion is provided. Simulation results show tha...

Goodness-of-fit Tests Based on the Kernel Density Estimator

Given an i.i.d. sample drawn from a density f on the real line, the problem of testing whether f is in a given class of densities is considered. Testing procedures constructed on the basis of minimizing the L1-distance between a kernel density estimate and any density in the hypothesized class are investigated. General non-asymptotic bounds are derived for the power of the test. It is shown tha...

On Goodness-of-fit Tests

Goodness-of-Fit Tests Based on Correcting Moments of Entropy Estimators

An Updated Review of Goodness of Fit Tests Based on Entropy

Different approaches to goodness of fit (GOF) testing are proposed. This survey intends to present the developments on Goodness of Fit based on entropy during the last 50 years, from the very first origins until the most recent advances for different data and models. Goodness of fit tests based on Shannon entropy was started by Vasicek in 1976 and were continued by many authors. In this paper, ...