Open Access
Issue |
ESAIM: PS
Volume 27, 2023
|
|
---|---|---|
Page(s) | 913 - 935 | |
DOI | https://doi.org/10.1051/ps/2023018 | |
Published online | 30 November 2023 |
- E. Afuecheta, A. Semeyutin, S. Chan, S. Nadarajah, and D.A.P. Ruiz, Compound distributions for financial returns. PLoS ONE 15 (2021), doi:10.1371/journal.pone.0239652. [Google Scholar]
- G.M. Allenby, R.P. Leone and L. Jen, A dynamic model of purchase timing with application to direct marketing. J. Am. Stat. Assoc. 94 (1999) 365–374. [CrossRef] [Google Scholar]
- S. Almalki and S. Nadarajah, Modifications of the Weibull distribution: a review. Reliabil. Eng. Syst. Saf. 124 (2014) 32–55. [CrossRef] [Google Scholar]
- H. Alzer, On some inequalities for the gamma and psi functions. Math. Comput. 66 (1997) 373–389. [CrossRef] [Google Scholar]
- N. Balakrishnan and S. Pal, An EM algorithm for the estimation of parameters of a flexible cure rate model with generalized gamma lifetime and model discrimination using likelihood- and information-based methods. Comput. Stat. 30 (2015) 151–189. [CrossRef] [Google Scholar]
- M. Bebbington, C.D. Lai and R. Zitikis, A flexible Weibull extension. Reliabil. Eng. Syst. Saf. 92 (2007) 719–726. [CrossRef] [Google Scholar]
- S. Bennette, Log-logistic regression models for survival data. Appl. Stat. 32 (1983) 165–171. [CrossRef] [Google Scholar]
- J.M.F. Carrasco, E.M.M. Ortega and G.M. Cordeiro, A generalized modified Weibull distribution for lifetime modeling. Comput. Stat. Data Anal. 53 (2008) 450–462. [CrossRef] [Google Scholar]
- A. Dadpay, E.S. Soofi and R. Soyer, Information measures for generalized gamma family. J. Econometrics 138 (2007) 568–585. [CrossRef] [MathSciNet] [Google Scholar]
- F.X. Diebold and G.D. Rudebusch, A nonparametric investigation of duration dependence in the American business cycle. J. Polit. Econ. 98 (1990) 596–616. [CrossRef] [Google Scholar]
- B. Dodson, The Weibull Analysis Handbook, 2nd edn. ASQ Quality Press (2006). [Google Scholar]
- B. Efron, Logistic regression, survival analysis, and the Kaplan-–Meier curve. J. Am. Stat. Assoc. 83 (1988) 414–425. [CrossRef] [Google Scholar]
- M. Gauss, G.M. Cordeiro, M.C. Lima, A.E. Gomes, C.Q. Silva and E.M. Ortega, The gamma extended Weibull distribution. J. Stat. Distrib. Applic. 3 (2016) 7. [CrossRef] [Google Scholar]
- R.E. Glaser, Bathtub and related failure rate characterizations. J. Am. Stat. Assoc. 75 (1980) 667–672. [CrossRef] [Google Scholar]
- O. Gomes, C. Combes and A. Dussauchoy, Parameter estimation of the generalized gamma distribution. Math. Comput. Simul. 79 (2008) 955–963. [CrossRef] [Google Scholar]
- I.S. Gradshteyn and I.M. Ryzhik, Table of Integrals, Series, and Products, 7th edn., edited by A. Jeffrey and D. Zwillinger. Academic Press, New York (2007). [Google Scholar]
- B.-N. Guo and Q. Feng, Refinements of lower bounds for polygamma functions. Proc. Am. Math. Soc. 141 (2013) 1007–1015. [Google Scholar]
- R.C. Gupta and W. Viles, Roller-coaster failure rates and mean residual life functions with application to the extended generalized inverse Gaussian models. Probab. Eng. Inform. Sci. 25 (2011) 103–118. [CrossRef] [Google Scholar]
- H.L. Harter, Maximum-likelihood estimation of the parameters of a four-parameter generalized gamma population from complete and censored samples. Technometrics 9 (1967) 159–165. [CrossRef] [MathSciNet] [Google Scholar]
- S. Kaniovski and M. Peneder, Determinants of firm survival: a duration analysis using the generalized gamma distribution. Empirica 35 (2008) 41–58. [CrossRef] [Google Scholar]
- S. Kullback and R. Leibler, On information and sufficiency. Ann. Math. Statist. 22 (1951) 79–86. [CrossRef] [Google Scholar]
- A.O. Langlands, S.J. Pocock, G.R. Kerr and S.M. Gore, Long-term survival of patients with breast cancer: a study of the curability of the disease. Br. Med. J. 2 (1979) 1247–1251. [CrossRef] [PubMed] [Google Scholar]
- C.-D. Lai, Generalized Weibull Distributions. Springer Science & Business Media (2013). [Google Scholar]
- C.D. Lai, M. Xie and D.N.P. Murthy, A modified Weibull distribution. Trans. Reliabil. 52 (2003) 33–37. [CrossRef] [Google Scholar]
- J.F. Lawless, Inference in the generalized gamma and log-gamma distributions. Technometrics 22 (1980) 409–419. [CrossRef] [MathSciNet] [Google Scholar]
- J.I. McCool, Using the Weibull Distribution: Reliability, Modeling and Inference. John Wiley & Sons Inc. (2012). [CrossRef] [Google Scholar]
- L.A. Medina and V.H. Moll, The integrals in Gradshteyn and Ryzhik. Part 10: THE Digamma function. Scientia A: Math. Sci. 17 (2009) 45–66. [Google Scholar]
- G.S. Mudholkar and D.K. Srivastava, Exponentiated Weibull family for analyzing bathtub failure-rate data. IEEE Trans. Reliabil. 42 (1993) 299–302. [CrossRef] [Google Scholar]
- G.S. Mudholkar, D.K. Srivastava and M. Freimer, The exponentiated Weibull family: a reanalysis of the bus-motor-failure data. Technometrics 37 (1995) 436–445. [CrossRef] [Google Scholar]
- G.S. Mudholkar, D.K. Srivastava and G.D. Kollia, A generalization of the Weibull distribution with application to the analysis of survival data. J. Am. Stat. Assoc. 91 (1996) 1575–1583. [CrossRef] [Google Scholar]
- I. Muqattash and M. Yahdi, Infinite family of approximations of the Digamma function. Math. Comput. Model. 43 (2006) 132–1336. [Google Scholar]
- D.N.P. Murthy, M. Xie and R. Jiang, Weibull Models. John Wiley & Sons Inc. (2004). [Google Scholar]
- S. Nadarajah and S. Kotz, On some recent modifications of Weibull distribution. IEEE Trans. Reliabil. 54 (2005) 561–562. [CrossRef] [Google Scholar]
- A. Noufaily and M.C. Jones, On maximization of the likelihood for the generalized gamma distribution. Comput. Stat. 28 (2013a) 505–517. [CrossRef] [Google Scholar]
- A. Noufaily and M.C. Jones, Parametric quantile regression based on the generalized gamma distribution. J. Roy. Stat. Soc. C (Appl. Stat.) 62 (2013b) 723–740. [CrossRef] [Google Scholar]
- E.M. Ortega, G.M. Cordeiro and M.W. Kattan, The log-beta Weibull regression model with application to predicting recurrence of prostate cancer. Stat. Pap. 54 (2013) 113–132. [CrossRef] [Google Scholar]
- C. Peng, R code for “A new formulation of generalized gamma: some results and applications” (2023). https://github.com/pengdsci/GG. [Google Scholar]
- T. Pham and J. Almhana, The generalized gamma distribution: its hazard rate and stress-strength model. IEEE Trans. Reliabil. 44 (1995) 392–397. [CrossRef] [Google Scholar]
- H. Pham and C.D. Lai, On recent generalizations of the Weibull distribution. IEEE Trans. Reliabil. 56 (2007) 454–458. [CrossRef] [Google Scholar]
- P.L. Ramos, A.L. Mota, P.H. Ferreira, E. Ramos, V.L.D. Tomazella and F. Louzada, Bayesian analysis of the inverse generalized gamma distribution using objective priors. J. Stat. Comput. Simul. 94 (2021) 786–816. [CrossRef] [MathSciNet] [Google Scholar]
- H. Rine, The Weibull Distribution: A Handbook. CRC Press (2008). [CrossRef] [Google Scholar]
- G.O. Silva, E.M. Ortega and G.M. Cordeiro, The beta modified Weibull distribution. Lifetime Data Anal. 16 (2010) 409–430. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
- N. Singla, K. Jain and S.K. Sharma, The beta generalized Weibull distribution: properties and applications. Reliabil. Eng. Syst. Saf. 102 (2012) 5–15. [CrossRef] [Google Scholar]
- E.W. Stacy, A generalization of the gamma distribution. Ann. Math. Stat. 33 (1962) 1187–1192. [CrossRef] [Google Scholar]
- E.W. Stacy and G.A. Mihram, Parameter estimation for a generalized gamma distribution. Technometrics 7 (1965) 349–358. [CrossRef] [MathSciNet] [Google Scholar]
- H. Xie and X. Wu, A conditional autoregressive range model with gamma distribution for financial volatility modelling. Econ. Model. 64 (2017) 349–356. [CrossRef] [Google Scholar]
- J. Xu and C. Peng, Fitting and testing the Marshall-Olkin extended Weibull model with randomly censored data. J. Appl. Stat. 41 (2014) 2577–2595. [CrossRef] [MathSciNet] [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.