Different Parameter Estimation Methods for Exponential Geometric Distribution and Its Applications in Lifetime Data Analysis

Feyza Günay and Mehmet Yilmaz
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Feyza Günay: Graduate School of Natural and Applied Sciences, Ankara University, Turkey
Mehmet Yilmaz: Graduate School of Natural and Applied Sciences, Ankara University, Turkey

Biostatistics and Biometrics Open Access Journal, 2018, vol. 8, issue 2, 36-43

Abstract: The new compound distributions which are started to be used with the study of Adamidis, et al. [1] still take place in recent studies. Exponential Geometric distribution, introduced by them, is a flexible distribution for modeling the lifetime data sets. They have used maximum likelihood method with expectation-maximization algorithm to estimate unknown parameters. In this paper, we use maximum likelihood and also least squares, weighted least squares, maximum product of spacings and l-moments methods to estimate the unknown parameters of exponential geometric distribution family. Then we compare the efficiency of these estimators via a simulation study for different sample sizes and parameter settings.

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:8:y:2018:i:2:p:36-43

DOI: 10.19080/BBOAJ.2018.08.555735

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