Estimation of Parameters of the Two-Parameter Rayleigh Distribution Based on Progressive Type-II Censoring Using Maximum Likelihood Method via the NR and the EM Algorithms
American Journal of Theoretical and Applied Statistics
Volume 6, Issue 1, January 2017, Pages: 1-9
Received: Nov. 15, 2016;
Accepted: Nov. 30, 2016;
Published: Dec. 20, 2016
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Murithi Daniel Fundi, Department of Statistics and Actuarial Science, Kenyatta University (KU), Nairobi, Kenya
Edward Gachangi Njenga, Department of Statistics and Actuarial Science, Kenyatta University (KU), Nairobi, Kenya
Kemboi George Keitany, Department of Statistics and Actuarial Science, Kenyatta University (KU), Nairobi, Kenya
In this article, Maximum likelihood estimates for the shape and scale parameters of two-parameter Rayleigh distribution are obtained based on progressive type-II censored samples using the Newton-Raphson (NR) method and the Expectation-Maximization (EM) algorithm. A simple algorithm discussed in [2-3] is used for generating progressive type-II censored samples. Based on this censoring scheme, approximate asymptotic variances are derived and used to construct approximate confidence intervals of the parameters. The performance of these two maximum likelihood estimation algorithms is compared in terms of simulation results of root mean squared error (RMSE) and the coverage rates. Simulation results showed that in nearly all the combination of simulation conditions the estimators based on the EM algorithm have less root mean squared error (RMSE) and narrower widths of confidence intervals compared to those obtained using the NR algorithm. Finally, an illustrative example with real-life data sets is provided to illustrate how maximum likelihood estimation using the two algorithms works in practice.
Murithi Daniel Fundi,
Edward Gachangi Njenga,
Kemboi George Keitany,
Estimation of Parameters of the Two-Parameter Rayleigh Distribution Based on Progressive Type-II Censoring Using Maximum Likelihood Method via the NR and the EM Algorithms, American Journal of Theoretical and Applied Statistics.
Vol. 6, No. 1,
2017, pp. 1-9.
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