Bicer, Cenker2020-06-252020-06-252018Bicer C. Statistical Inference for Geometric Process with the Power Lindley Distribution. Entropy. 2018; 20(10):723.1099-4300https://doi.org/10.3390/e20100723https://hdl.handle.net/20.500.12587/7288The geometric process (GP) is a simple and direct approach to modeling of the successive inter-arrival time data set with a monotonic trend. In addition, it is a quite important alternative to the non-homogeneous Poisson process. In the present paper, the parameter estimation problem for GP is considered, when the distribution of the first occurrence time is Power Lindley with parameters alpha and lambda. To overcome the parameter estimation problem for GP, the maximum likelihood, modified moments, modified L-moments and modified least-squares estimators are obtained for parameters a, alpha and lambda. The mean, bias and mean squared error (MSE) values associated with these estimators are evaluated for small, moderate and large sample sizes by using Monte Carlo simulations. Furthermore, two illustrative examples using real data sets are presented in the paper.eninfo:eu-repo/semantics/openAccessgeometric processmaximum likelihood estimatemodified moment estimatemodified L-moment estimatemodified least-square estimateArticle201010.3390/e201007232-s2.0-8505570286633265812Q1WOS:000448545700004Q2