A Simulation Study Goodness-of-Fit Tests for the Skewed Normal Distribution

2013 ◽  
pp. 277-283
Author(s):  
Emre E. Sarısoy ◽  
Nihan Potas ◽  
Mahmut Kara
Keyword(s):  
Normal Distribution ◽  
Simulation Study ◽  
Goodness Of Fit ◽  
Goodness Of Fit Tests ◽  
Skewed Normal Distribution

Comparison of Weibull and normal distributions for concrete compressive strengths

2006 ◽  
Vol 33 (10) ◽  
pp. 1287-1292 ◽  
Author(s):  
P J Tumidajski ◽  
L Fiore ◽  
T Khodabocus ◽  
M Lachemi ◽  
R Pari
Keyword(s):  
Compressive Strength ◽  
Weibull Distribution ◽  
Normal Distribution ◽  
Goodness Of Fit ◽  
Theoretical Description ◽  
Normal Distributions ◽  
Failure Data ◽  
Goodness Of Fit Tests ◽  
Strength Failure ◽  
The Difference

For concrete produced in a commercial ready mix operation, the compressive strengths were fitted to Weibull and normal distributions. It was found that the Weibull distribution successfully describes concrete compressive strength failure data. This information is useful in the theoretical description of concrete failure. Furthermore, based on chi-squared, Anderson–Darling and Kolmogorov-Smirnov goodness-of-fit tests, the difference between the Weibull and normal distribution is not large enough to make a clear distinction regarding which distribution definitively fits the experimental data better. Key words: compressive strength, normal distribution, Weibull distribution, goodness-of-fit.

scholarly journals Testing normality of chosen R-estimates used in deformation analysis

2020 ◽  
Vol 10 (1) ◽  
pp. 7-13
Author(s):  
R. Duchnowski ◽  
P. Wyszkowska
Keyword(s):  
Normal Distribution ◽  
Goodness Of Fit ◽  
Functional Model ◽  
Point Of View ◽  
Deformation Analysis ◽  
Geodetic Observation ◽  
Goodness Of Fit Tests ◽  
Linear Estimators ◽  
Independent Observations ◽  
Tests For Normality

AbstractThe normal distribution is one of the most important distribution in statistics. In the context of geodetic observation analyses, such importance follows Hagen’s hypothesis of elementary errors; however, some papers point to some leptokurtic tendencies in geodetic observation sets. In the case of linear estimators, the normality is guaranteed by normality of the independent observations. The situation is more complex if estimates and/or the functional model are not linear. Then the normality of such estimates can be tested theoretically or empirically by applying one of goodness-of-fit tests.This paper focuses on testing normality of selected variants of the Hodges-Lehmann estimators (HLE). Under some general assumptions the simplest HLEs have asymptotical normality. However, this does not apply to the Hodges-Lehmann weighted estimators (HLWE), which are more applicable in deformation analysis. Thus, the paper presents tests for normality of HLEs and HLWEs. The analyses, which are based on Monte Carlo method and the Jarque–Bera test, prove normality of HLEs. HLWEs do not follow the normal distribution when the functional model is not linear, and the accuracy of observation is relatively low. However, this fact seems not important from the practical point of view.

scholarly journals Comparison of Goodness of Fit Tests for Normal Distribution

2018 ◽  
pp. 1-32
Author(s):  
I. Agu, Friday ◽  
E. Francis, Runyi
Keyword(s):  
Normal Distribution ◽  
Goodness Of Fit ◽  
P Value ◽  
Goodness Of Fit Test ◽  
Chi Square ◽  
Goodness Of Fit Tests ◽  
Kolmogorov Smirnov

Goodness of fit test is a test that has attracted researchers’ interest over the decades. This study is on goodness of fit test for normal distribution only. The Kolmogorov-Smirnov (K-St) and Pearson’s Chi-square (χ² test) goodness of fit test were used to determine the normality of a given data.  The result revealed that the data is normal under the two tests and that the Kolmogorov-Smirnov (K-S test) were preferred to Pearson’s Chi-square (χ² test). The Kolmogorov-Smirnov (K-S) test of goodness of fit is the most suitable in terms of the p-value.  

scholarly journals Data driven efficient score tests for Poissonity

2019 ◽  
Vol 39 (1) ◽  
pp. 115-126
Author(s):  
Tadeusz Inglot
Keyword(s):  
Poisson Distribution ◽  
Simulation Study ◽  
Goodness Of Fit ◽  
Data Driven ◽  
General Construction ◽  
Efficient Score ◽  
Score Tests ◽  
Goodness Of Fit Tests ◽  
Composite Hypotheses

New data driven score tests for testing goodness of fit of the Poisson distribution are proposed. They are direct applications of the general construction of data driven goodness-of-fit tests for composite hypotheses developed in Inglot et al. 1997. By a simulation study it is shown that these tests perform almost equally well as the best known solutions for standard alternatives and outperform them for more difficult alternatives.

Sign in / Sign up

Export Citation Format

Share Document