Estimator Cramer Von Mises bagi Parameter Distribusi Kumaraswamy-Lindley

Bagus Arya Saputra, Zani Anjani Rafsanjani

Abstract

The Kumaraswamy-Lindley (KL) distribution is a combination of the Lindley distribution and the Kumaraswamy distribution. The KL distribution is widely used to examine lifetime data. The importance of the application of the KL distribution in explaining lifetime data makes it necessary to estimate distribution parameters well. Therefore, this research will discuss the Cramer Von Mises Estimator (ECM) for the Kumaraswamy-Lindley distribution parameters. The formula for the ECM is obtained and the simulation is carried out using the same initial parameters with different generation sample sizes. The simulation results show that for the same initial parameters, estimation with a larger sample size has better results.

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References

Lindley, D.V. “Fudicial distributions and Bayes theorem”. Journal of the Royal Statistical Society B 20:102107. 1958.

P. Kumaraswamy, “A generalized probability density function for double-bounded random processes,” Journal of Hydrology, vol. 46, no. 1, pp. 79-88, 1980.

G. M. Cordeiro and M. de Castro, “A new family of generalized distributions,” J. Statist. Comput Simul, vol. 81, pp. 883898, 2011.

F. Merovci and V. Sharma, “The Kumaraswamy Lindley distribution: properties and applications”, 2014.

S. Cakmakyapan, G. Ozel, Y. M. H. El Gebaly, and G. G. Hamedani, “The Kumaraswamy Marshall-Olkin log-logistic distribution with application,” Journal of Statistical Theory and Applications, vol. 17, no. 1, pp. 59–76, 2017.

B. O. Oluyede, T. Yang, and B. Omolo, “A generalized class of Kumaraswamy Lindley distribution with applications to lifetime data,” Journal of Computations & Modelling, vol. 5, no. 1, pp. 7-70, 2015.

S. Abdelmoezz and S. M. Mohamed, “New mathematical properties of the Kumaraswamy Lindley distribution,” Indonesian Journal of Applied Statistics, vol. 1, no. 1, pp. 67-77, 2022.

G. Casela and R. L. Berger, Statistical inference, Ed. 2nd, USA: Brooks/Cole Cengage Learning, 2002.

S. Ali, D. Sanku, M. H. Tahir, and M. Mansoor, “The comparison of different estimation methods for the parameters of flexible weibull distribution,” Communications Faculty of Sciences University of Ankara Series A-Mathematics and Statistics, vol. 69, no. 1, pp. 794-814, 2020. doi:10.31801/cfsuasmas.597680

R. M. Neal, “Slice Sampling,” The Annals of Statistics, vol. 31, no. 3, 2003.

R. E. Walpole, R. H. Myers, S. L. Myers, and K. Ye, Probability & statistics for engineers & ecientists, 9th Edition. Pearson Education, Inc., 2007.

DOI: https://doi.org/10.13057/ijas.v7i1.79911

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