Open Access
Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 14, 2023
Article Number 10
Number of page(s) 10
DOI https://doi.org/10.1051/smdo/2023011
Published online 26 September 2023
  1. L. Amoroso, Ricerche intorno alla curva dei redditi, Ann. Mat. Pura Appl. 2, 123–159 (1925) [CrossRef] [MathSciNet] [Google Scholar]
  2. E.W. Stacy, A generalization of the gamma distribution, Ann. Math. Stat. 33, 1187–1192 (1962) [CrossRef] [Google Scholar]
  3. N.L. Johnson, S. Kotz, N. Balakrishan, Continuous Univariate Distributions ( Wiley, New York, 1994), Vol. 2 [Google Scholar]
  4. S.K. Agarwal, J.A. Al-Saleh, Generalized gamma type distribution and its hazard rate function, Commun. Stat. −Theory Methods. 30, 309–318 (2001) [CrossRef] [Google Scholar]
  5. N. Balakrishnan, Y. Peng, Generalized gamma frailty model, Stat. Med. 25, 2797–816 (2006) [CrossRef] [MathSciNet] [Google Scholar]
  6. S. Nadarajah, A.K. Gupta, Statistical tools for drop size distributions: moments and generalized gamma. Math. Comput. Simul. 74, 1–7 (2007) [CrossRef] [Google Scholar]
  7. L.G. Pinho, G.M. Cordeiro, J.S. Nobre, The gamma-exponentiated Weibull distribution, J. Stat. Theory Appl. 11, 379–395 (2012) [Google Scholar]
  8. S. Jaggia, Specification tests based on the heterogeneous generalized gamma model of duration: with an application to Kennan's strike data, J. Appl. Econom. 6, 169–180 (1991) [CrossRef] [Google Scholar]
  9. M. Khodabina, A. Ahmadabadib, Some properties of generalized gamma distribution, Math. Sci. 4, 9–28 (2010) [MathSciNet] [Google Scholar]
  10. J. Kiche, O. Ngesa, G. Orwa, On generalized gamma distribution and its application to survival data, Int. J. Stat. Probab. 8, 85–102 (2019) [Google Scholar]
  11. O. Gomès, C. Combes, A. Dussauchoy, Parameter estimation of the generalized gamma distribution, Math. Comput. Simul. 79, 955–963 (2008) [CrossRef] [Google Scholar]
  12. B. Lagos Álvarez, G. Ferreira, M. Valenzuela Hube, A proposed reparametrization of gamma distribution for the analysis of data of rainfall-runoff driven pollution, Proyecciones (Antofagasta) 30, 415–439 (2011) [CrossRef] [Google Scholar]
  13. R. Vani Lakshmi, V.S. Vaidyanathan, Three-parameter gamma distribution: estimation using likelihood, spacings and least squares approach, J. Stat. Manag. Sys. 19, 37–53 (2016) [Google Scholar]
  14. H. Abubakar, S.R.M. Sabri, A simulation study on modified Weibull distribution for modelling of investment return, Pertanika J. Sci. Technol. 29, 2767–2790 (2021) [CrossRef] [Google Scholar]
  15. V.S. Özsoy, M.G. Ünsal, H.H. Örkcü, Use of the heuristic optimization in the parameter estimation of generalized gamma distribution: comparison of GA, DE, PSO and SA methods, Comput. Stat. 35, 1895–1925 (2020) [Google Scholar]
  16. S.M. Stigler, The history of statistics: the measurement of uncertainty before 1900 (Harvard Uni. Press, 1986) [Google Scholar]
  17. M.J. Zyphur, F.L. Oswald, Bayesian estimation and inference: a user's guide, J. Manag. 41, 390−420 (2015) [Google Scholar]
  18. H. Abubakar, S.R.M. Sabri, Weibull distribution for claims modelling: a Bayesian approach. in: 2022 International Conference on Decision Aid Sciences and Applications (DASA) 2022 Mar 23, IEEE, pp. 108–112 [Google Scholar]
  19. K. Łatuszyński, G.O. Roberts, J.S. Rosenthal, Adaptive Gibbs samplers and related MCMC methods, Ann. Appl. Probab. 23, 66–98 (2013) [MathSciNet] [Google Scholar]
  20. S. Geman, D. Geman Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images, IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984) [CrossRef] [Google Scholar]
  21. W.K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika. 57, 97–109 (1970) [CrossRef] [MathSciNet] [Google Scholar]
  22. A.E. Gelfand, A.F. Smith, Sampling-based approaches to calculating marginal densities, J. Am. Stat. Assoc. 85 (410), 398–409 (1990) [CrossRef] [Google Scholar]
  23. W.R. Gilks, Derivative-free adaptive rejection sampling for Gibbs sampling, Bayesian Stat. 4 (2), 641–649 (1992) [Google Scholar]
  24. W.R. Gilks, P. Wild, Adaptive rejection sampling for Gibbs sampling, J. R. Stat. Soc., C: Appl. Stat. 41, 337–348 (1992) [Google Scholar]
  25. L. Martino, J. Read, D. Luengo, Independent doubly adaptive rejection Metropolis sampling within Gibbs sampling. IEEE Trans. Signal Process. 63, 3123–3138 (2015) [CrossRef] [MathSciNet] [Google Scholar]
  26. R.B. Miller, Bayesian analysis of the two-parameter gamma distribution, Technometrics. 22, 65–69 (1980) [CrossRef] [Google Scholar]
  27. B. Pradhan, D. Kundu, Bayes estimation and prediction of the two-parameter gamma distribution, J. Stat. Comput. Simul. 81, 1187–1198 (2011) [CrossRef] [MathSciNet] [Google Scholar]
  28. S.K. Upadhyay, M. Peshwani, Full posterior analysis of three parameter lognormal distribution using Gibbs sampler, J. Stat. Comput. Simul. 71, 215–230 (2001) [CrossRef] [Google Scholar]
  29. C.W. Wu, M.H. Shu, T.Y. Huang, B.M. Hsu, Comparisons of frequentist and Bayesian inferences for interval estimation on process yield, J. Oper. Res. Soc. 9, 1–2 (2021) [CrossRef] [MathSciNet] [Google Scholar]
  30. N. Cappuccio, D. Lubian, D. Raggi, MCMC Bayesian estimation of a skew-GED stochastic volatility model, Stud. Nonlinear Dyn. Econom. 8, (2004) [Google Scholar]
  31. Y.S. Son, M. Oh, Bayesian estimation of the two-parameter Gamma distribution, Commun. Stat. −Simul. Comput. 35, 285–293 (2006) [CrossRef] [Google Scholar]
  32. M.R. Oh, K.S. Kim, W.H. Cho, Y.S. Son, Bayesian parameter estimation of the four-parameter gamma distribution, Commun. Stat. Appl. Methods. 14, 255–266 (2007) [Google Scholar]
  33. P.L. Ramos, F. Louzada, Bayesian reference analysis for the generalized gamma distribution, IEEE Commun. Lett. 22, 1950–1953 (2018) [CrossRef] [Google Scholar]
  34. J. Tang, B. Fan, L. Xiao, S. Tian, F. Zhang, L. Zhang, D. Weitz, A new ensemble machine-learning framework for searching sweet spots in shale reservoirs, SPE J. 26, 482–497 (2021) [CrossRef] [Google Scholar]
  35. G. Lee, W. Kim, H. Oh, B.D. Youn, N.H. Kim, Review of statistical model calibration and validation—from the perspective of uncertainty structures, Struct. Multidiscip. Optim. 60, 1619–1644 (2019) [CrossRef] [MathSciNet] [Google Scholar]
  36. X. Shang, H.K. Ng, On parameter estimation for the generalized gamma distribution based on left‐truncated and right‐censored data, Comput. Math. Methods. 3, e1091 (2021) [CrossRef] [Google Scholar]
  37. A.I.A. Sayed, S.R.M. Sabri, A simulation study on the simulated annealing algorithm in estimating the parameters of generalized gamma distribution, Sci. Technol. Indones. 27, 84–90 (2022) [Google Scholar]
  38. A.I.A. Sayed, S.R.M. Sabri, Transformed modified internal rate of return on gamma distribution for long term stock investment modelling, J. Manag. Inf. Decis. Sci. 25, 1–7(2022) [Google Scholar]
  39. H. Abubakar, S.R. Sabri, Incorporating simulated annealing algorithm in the Weibull distribution for valuation of investment return of Malaysian property development sector, Int. J. Simul. Multidiscip. Des. Optim. 12, 22 (2021) [CrossRef] [EDP Sciences] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.