method of moments estimator for geometric distribution

Survival analysis includes modeling the time until the occurrence of an event of interest. Keywords: Exponential geometric distribution Maximum likelihood Least squares Weighted least squares Maximum product of spacings I-moments Lifetime data analysisĪbbrevations:EG: Exponential Geometric EP: Exponential Poisson DFR: Decreasing Failure Rate PE: Poisson-Exponential CEG: Complementary Exponential Geometric ML: Maximum Likelihood EM: Expectation-Maximization LS: Least Squares WLS: Weighted Least Squares MPS: Maximum Product of Spacings LM: L-moments AIC: Akaike Information Criterion At the end of the study, two lifetime data sets such as coal mine data and medical data about occupational safety and duration hospitalization studies are illustrated for application.

Then we compare the efficiency of these estimators via a simulation study for different sample sizes and parameter settings. 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. They have used maximum likelihood method with expectation-maximization algorithm to estimate unknown parameters. Exponential Geometric distribution, introduced by them, is a flexible distribution for modeling the lifetime data sets.

The new compound distributions which are started to be used with the study of Adamidis, et al.

Method of moments estimator for geometric distribution