Parameter estimation in the exponential distribution, confidence intervals and a monte carlo study for a goodness of fit test

1972 ◽  
Vol 13 (3) ◽  
pp. 225-246 ◽  
Author(s):  
R. M. J. Heuts
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Exponential Distribution ◽  
Confidence Intervals ◽  
Goodness Of Fit ◽  
Monte Carlo Study ◽  
Goodness Of Fit Test

scholarly journals A simple non-parametric goodness-of-fit test for elliptical copulas

2017 ◽  
Vol 5 (1) ◽  
pp. 330-353 ◽  
Author(s):  
Miriam Jaser ◽  
Stephan Haug ◽  
Aleksey Min
Keyword(s):  
Monte Carlo ◽  
Goodness Of Fit ◽  
Monte Carlo Study ◽  
Goodness Of Fit Test ◽  
Kendall’S Tau ◽  
Kendall's Tau ◽  
Nominal Level ◽  
Empirical Application ◽  
Non Parametric

AbstractIn this paper, we propose a simple non-parametric goodness-of-fit test for elliptical copulas of any dimension. It is based on the equality of Kendall’s tau and Blomqvist’s beta for all bivariate margins. Nominal level and power of the proposed test are investigated in a Monte Carlo study. An empirical application illustrates our goodness-of-fit test at work.

A test for fuzzy exponentiality based on Kullback-Leibler information

2021 ◽  
pp. 1-8
Author(s):  
Lingtao Kong
Keyword(s):  
Biological Sciences ◽  
Monte Carlo ◽  
Goodness Of Fit ◽  
Experimental Studies ◽  
Real Data ◽  
Test Statistics ◽  
Test Statistic ◽  
Goodness Of Fit Test ◽  
Higher Power ◽  
Leibler Information

The exponential distribution has been widely used in engineering, social and biological sciences. In this paper, we propose a new goodness-of-fit test for fuzzy exponentiality using α-pessimistic value. The test statistics is established based on Kullback-Leibler information. By using Monte Carlo method, we obtain the empirical critical points of the test statistic at four different significant levels. To evaluate the performance of the proposed test, we compare it with four commonly used tests through some simulations. Experimental studies show that the proposed test has higher power than other tests in most cases. In particular, for the uniform and linear failure rate alternatives, our method has the best performance. A real data example is investigated to show the application of our test.

scholarly journals New goodness-of-fit test for exponentiality based on a conditional moment characterisation

ORiON ◽  
2019 ◽  
Vol 35 (2) ◽  
pp. 145-160
Author(s):  
M Smuts ◽  
JS Allison ◽  
L Santana
Keyword(s):  
Exponential Distribution ◽  
Queuing Theory ◽  
Goodness Of Fit ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Real World Data ◽  
Conditional Moment ◽  
Goodness Of Fit Test ◽  
Underlying Assumption

The exponential distribution plays a key role in the practical application of reliability theory, survival analysis, engineering and queuing theory. These applications often rely on the underlying assumption that the observed data originate from an exponential distribution. In this paper, two new tests for exponentiality are proposed, which are based on a conditional second moment characterisation. The proposed tests are compared to various established tests for exponentiality by means of a simulation study where it is found that the new tests perform favourably relative to the existing tests. The tests are also applied to real-world data sets with independent and identically distributed data as well as to simulated data from a Cox proportional hazards model, to determine whether the residuals obtained from the fitted model follow a standard exponential distribution.

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