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American Journal of Applied Mathematics and Statistics. 2016, 4(1), 1-8
DOI: 10.12691/AJAMS-4-1-1
Original Research

Goodness-of-fit-test for Exponential Power Distribution

A. A. Olosunde1, and A. M. Adegoke2,

1Department of Mathematics, Obafemi Awolowo University, Ile-Ife, Nigeria

2Department of Physics, Obafemi Awolowo University, Ile-Ife, Nigeria

Pub. Date: January 15, 2016
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Cite this paper

A. A. Olosunde and A. M. Adegoke. Goodness-of-fit-test for Exponential Power Distribution. American Journal of Applied Mathematics and Statistics. 2016; 4(1):1-8. doi: 10.12691/AJAMS-4-1-1

Abstract

Given a set of data, one of the statistical issues is to see how well the data fit into postulated model. This technique necessitates the corresponding table of the probability distribution for the proposed model. In this paper, we examined, to what limit of p can normal approximate this sample without falling into type I error (i.e. a random variable x having normal distribution when indeed it has exponential power distribution with estimated parameter p). We also present the goodness-of-fit test for exponential power distribution using the conventional testing methods which are discussed, one is Pearson’s χ2 test and the other one is kolmogorov-Smirnov test. An example in poultry feeds data and a simulation example are included, comparison with the fitting of the normal distribution is also examined for further illustration.

Keywords

shape parameter, short tails, cumulative distribution function, kolmogorov-Smirnov test, pearson’s χ2 test, poultry feeds data data

Copyright

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

[1]  Agro, G. (1992), Maximum Likelihood and Lp-norm Estimators. Statistica Applicata. 4(2), 171-182.
 
[2]  Agro, G. (1995), Maximum Likelihood Estimation for the Exponential Power Distribution. Communications in Statistics (Simulation and Computation), 24(2), 523-536.
 
[3]  Ihaka, R and Gentleman, R. (1996), R. A Language for Data Analysis and Graphics. Journal of Computational and Graphical Statistics. Vol. 4(3), pp. 299-314.
 
[4]  Lindsey, J.K. (1999). Multivariate Elliptical Contoured Distributions for Repeated Measurements. Biometrics 55, 1277-1280.
 
[5]  Miller, L.H. (1956), Table of Percentage Points of Kolmogorov Statistics. Journal of the American Statistical Association. Vol. 51, 111-121.
 
[6]  Mineo, A.M. and Ruggieri, M. (2005). A Software Tool for the Exponential Power Distribution: The normalp package. Journal of Statistical Software. Vol. 12(4), pp, 1-24.
 
[7]  Olosunde, A.A. (2013). On Exponential Power Distribution And Poultry Feeds Data: A Case Study. Journal Iran Statistical Society. Vol. 12(2), pp. 253-270.