Estimation of the parameters of the gamma geometric process

Kara M., GÜVEN G., ŞENOĞLU B., AYDOĞDU H.

Journal of Statistical Computation and Simulation, vol.92, no.12, pp.2525-2535, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 92 Issue: 12
  • Publication Date: 2022
  • Doi Number: 10.1080/00949655.2022.2040501
  • Journal Name: Journal of Statistical Computation and Simulation
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Business Source Elite, Business Source Premier, CAB Abstracts, Communication Abstracts, Metadex, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.2525-2535
  • Keywords: Geometric process, gamma distribution, modified maximum likelihood, asymptotic normality, Monte Carlo simulation, STATISTICAL-INFERENCE, MODEL, SYSTEM
  • Eskisehir Osmangazi University Affiliated: Yes

Abstract

© 2022 Informa UK Limited, trading as Taylor & Francis Group.There is no doubt that finding the estimators of model parameters accurately and efficiently is very important in many fields. In this study, we obtain the explicit estimators of the unknown model parameters in the gamma geometric process (GP) via the modified maximum likelihood (MML) methodology. These estimators are as efficient as maximum likelihood (ML) estimators. The marginal and joint asymptotic distributions of the MML estimators are also derived and efficiency comparisons between ML and MML estimators are made through an extensive Monte Carlo simulations. Moreover, a real data example is considered to illustrate the performances of the MML estimators together with their ML counterparts. According to simulation results, the performances of MML and ML estimators are close to each other even for small sample sizes.