Testing Goodness-of-Fit with the Kernel Density Estimator:GoFKernel
نویسندگان
چکیده
منابع مشابه
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...
متن کاملGoodness 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...
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To test the hypothesis H0 : f = ψ that an unknown density f is equal to a specified one, ψ, an estimate f̂ of f is compared with ψ. The total variation distance ||f̂ − ψ||1 is used as test statistic. The density estimate f̂ considered is a peculiar one. A table of critical values is provided, this table is applicable for arbitrary ψ. Relations with other methods, Neyman’s smooth tests in particula...
متن کاملA Kernel Test of Goodness of Fit
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...
متن کاملGoodness-of-fit testing under long memory
In this talk we shall discuss the problem of fitting a distribution function to the marginal distribution of a long memory process. It is observed that unlike in the i.i.d. set up, classical tests based on empirical process are relatively easy to implement. More importantly, we discuss fitting the marginal distribution of the error process in location, scale and linear regression models. An int...
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ژورنال
عنوان ژورنال: Journal of Statistical Software
سال: 2015
ISSN: 1548-7660
DOI: 10.18637/jss.v066.c01