Algorithm AS 248: Empirical Distribution Function Goodness-of-Fit Tests

1989 ◽  
Vol 38 (3) ◽  
pp. 535 ◽  
Author(s):  
Charles S. Davis ◽  
Michael A. Stephens
Keyword(s):  
Distribution Function ◽  
Goodness Of Fit ◽  
Empirical Distribution Function ◽  
Empirical Distribution ◽  
Goodness Of Fit Tests

scholarly journals Entropy Estimation From Ranked Set Samples With Application to Test of Fit

2017 ◽  
Vol 40 (2) ◽  
pp. 223-241 ◽  
Author(s):  
Ehsan Zamanzade ◽  
Mahdi Mahdizadeh
Keyword(s):  
Distribution Function ◽  
Goodness Of Fit ◽  
Empirical Distribution Function ◽  
Empirical Distribution ◽  
The Other ◽  
Inverse Gaussian ◽  
Nonparametric Maximum Likelihood ◽  
Mean Squared Errors ◽  
Entropy Estimation ◽  
Goodness Of Fit Tests

This article deals with entropy estimation using ranked set sampling (RSS). Some estimators are developed based on the empirical distribution function and its nonparametric maximum likelihood competitor. The suggested entropy estimators have smaller root mean squared errors than the other entropy estimators in the literature. The proposed estimators are then used to construct goodness of fit tests for inverse Gaussian distribution.

scholarly journals A regression characterization of inverse Gaussian distributions and application to EDF goodness-of-fit tests

2003 ◽  
Vol 2003 (9) ◽  
pp. 587-592
Author(s):  
Khoan T. Dinh ◽  
Nhu T. Nguyen ◽  
Truc T. Nguyen
Keyword(s):  
Distribution Function ◽  
Density Function ◽  
Goodness Of Fit ◽  
Empirical Distribution Function ◽  
Empirical Distribution ◽  
Inverse Gaussian ◽  
Gaussian Distributions ◽  
Goodness Of Fit Tests ◽  
Sample Application

We give a new characterization of inverse Gaussian distributions using the regression of a suitable statistic based on a given random sample. A corollary of this result is a characterization of inverse Gaussian distribution based on a conditional joint density function of the sample. Application of this corollary as a transformation in the procedure to construct EDF (empirical distribution function) goodness-of-fit tests for inverse Gaussian distributions is also studied.

scholarly journals ON APPROXIMATING THE DISTRIBUTIONS OF GOODNESS-OF-FIT TEST STATISTICS BASED ON THE EMPIRICAL DISTRIBUTION FUNCTION: THE CASE OF UNKNOWN PARAMETERS

2009 ◽  
Vol 12 (02) ◽  
pp. 157-167 ◽  
Author(s):  
MARCO CAPASSO ◽  
LUCIA ALESSI ◽  
MATTEO BARIGOZZI ◽  
GIORGIO FAGIOLO
Keyword(s):  
Monte Carlo ◽  
Distribution Function ◽  
Goodness Of Fit ◽  
Empirical Distribution Function ◽  
Empirical Distribution ◽  
Test Statistics ◽  
Test Statistic ◽  
Unknown Parameters ◽  
Goodness Of Fit Test ◽  
The Impact

This paper discusses some problems possibly arising when approximating via Monte-Carlo simulations the distributions of goodness-of-fit test statistics based on the empirical distribution function. We argue that failing to re-estimate unknown parameters on each simulated Monte-Carlo sample — and thus avoiding to employ this information to build the test statistic — may lead to wrong, overly-conservative. Furthermore, we present some simple examples suggesting that the impact of this possible mistake may turn out to be dramatic and does not vanish as the sample size increases.

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