On goodness-of-fit tests for the two-parameter Weibull distribution derived from the stabilized probability plot

Biometrika ◽  
1989 ◽  
Vol 76 (3) ◽  
pp. 593-598 ◽  
Author(s):  
STUART G. COLES
Keyword(s):  
Weibull Distribution ◽  
Goodness Of Fit ◽  
Goodness Of Fit Tests ◽  
Probability Plot ◽  
Two Parameter

The use of Weibull Statistics to Quantify Property Variability in Fe-3Mn-0.8C Sinter-Hardened Structurally Inhomogeneous Steels

2013 ◽  
Vol 58 (4) ◽  
pp. 1045-1052 ◽  
Author(s):  
A. Cias ◽  
A. Czarski
Keyword(s):  
Tensile Strength ◽  
Weibull Distribution ◽  
Goodness Of Fit ◽  
Rupture Strength ◽  
Low Carbon ◽  
Weibull Statistics ◽  
Transverse Rupture Strength ◽  
Iron Powders ◽  
Goodness Of Fit Tests ◽  
Two Parameter

Abstract Low carbon ferro-manganese and graphite powders were admixed to Hoganas sponge, NC100.24, and water atomised, ABC 100.30 and ASC 100.29, iron powders - to produce three variants of sintered Fe-3Mn-0.8C steel. These were pressed into tensile and bend specimens at 660 MPa, sintered in semi-closed containers for 1 hour in dry nitrogen or hydrogen at 1120 or 1250°C and cooled at 64°C/min. Both tensile strength and transverse rupture strength were examined using Weibull statistics. This paper presents the results of a study to develop and evaluate goodness of fit tests for the two- and three-parameter Weibull distributions. The study was initiated because of discrepancies in published critical values for two-parameter Weibull distribution goodness of fit tests and the lack of general three-parameter Weibull distribution goodness of fit tests for properties of PM steels.

scholarly journals comparison of methods for the estimation of Weibull distribution parameters

2014 ◽  
Vol 11 (1) ◽  
Author(s):  
Felix Nwobi ◽  
Chukwudi Ugomma
Keyword(s):  
Weibull Distribution ◽  
Stock Prices ◽  
Goodness Of Fit ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Goodness Of Fit Tests ◽  
Distribution Parameters ◽  
Kolmogorov Smirnov ◽  
The Mean ◽  
Maximum Likelihood Estimation Method

In this paper we study the different methods for estimation of the parameters of the Weibull distribution. These methods are compared in terms of their fits using the mean square error (MSE) and the Kolmogorov-Smirnov (KS) criteria to select the best method. Goodness-of-fit tests show that the Weibull distribution is a good fit to the squared returns series of weekly stock prices of Cornerstone Insurance PLC. Results show that the mean rank (MR) is the best method among the methods in the graphical and analytical procedures. Numerical simulation studies carried out show that the maximum likelihood estimation method (MLE) significantly outperformed other methods.

Simplified Likelihood Based Goodness-of-fit Tests for the Weibull Distribution

2015 ◽  
Vol 45 (3) ◽  
pp. 920-951 ◽  
Author(s):  
Meryam Krit ◽  
Olivier Gaudoin ◽  
Min Xie ◽  
Emmanuel Remy
Keyword(s):  
Weibull Distribution ◽  
Goodness Of Fit ◽  
Goodness Of Fit Tests
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