Article detail · 2022
Estimation of the parameters of the gamma geometric process
Journal of Statistical Computation and Simulation
- Year
- 2022
- ISSN
0094-9655- Type
- article
Data source split
- YÖKSİS YÖKSİS article record
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
English (OpenAlex)
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.
Topics
- Mathematical Approximation and Integration
- Manufacturing Process and Optimization
- Optimization and Packing Problems
Primary topic Mathematical Approximation and Integration