×

Fiducial inference for Birnbaum-Saunders distribution. (English) Zbl 1510.62415

Summary: In this paper, we consider fiducial inference for the unknown parameters of the Birnbaum-Saunders distribution. Two generalized fiducial distributions of the parameters are obtained. One is based on the inverse of the structural equation, and the fiducial estimates of the parameters are obtained by a simulation method. The other is based on the method of J. Hannig [Stat. Sin. 23, No. 2, 489–514 (2013; Zbl 1379.62002)], then we use adaptive rejection Metropolis sampling to get the fiducial estimates. We compare the fiducial estimates with the maximum likelihood estimates and Bayesian estimates by simulations. Two real data sets are analysed for illustration.

MSC:

62N05 Reliability and life testing
62F25 Parametric tolerance and confidence regions
62F10 Point estimation
62F15 Bayesian inference

Citations:

Zbl 1379.62002

Software:

R; LBFGS-B; dlm
Full Text: DOI

References:

[1] Birnbaum ZW, Saunders SC. A new family of life distribution. J. Appl. Probab. 1969;6:319-327. doi: 10.2307/3212003[Crossref], [Web of Science ®], [Google Scholar] · Zbl 0209.49801
[2] Desmond AF. Stochastic models of failure in random environments. Canad. J. Statist. 1985;13:171-183. doi: 10.2307/3315148[Crossref], [Web of Science ®], [Google Scholar] · Zbl 0581.60073
[3] Birnbaum ZW, Saunders SC. Estimation for a family of life distributions with applications to fatigue. J. Appl. Probab. 1969b;6:328-347. doi: 10.2307/3212004[Crossref], [Web of Science ®], [Google Scholar] · Zbl 0216.22702
[4] Engelhardt M, Bain LJ, Wright FT. Inferences on the parameters of the Birnbaum-Saunders fatigue life distribution based on maximum likelihood estimation. Technometrics. 1981;23:251-255. doi: 10.2307/1267788[Taylor & Francis Online], [Web of Science ®], [Google Scholar] · Zbl 0462.62077
[5] Rieck JR, Nedelman JR. A log-linear model for the Birnbaum-Saunders distribution. Technometrics. 1991;33:51-60. [Taylor & Francis Online], [Web of Science ®], [Google Scholar] · Zbl 0717.62090
[6] Lu M, Chang DS. Bootstrap prediction intervals for the Birnbaum-Saunders distribution. Microelect. Reliabil. 1997;37:1213-1216. doi: 10.1016/S0026-2714(96)00296-X[Crossref], [Web of Science ®], [Google Scholar]
[7] Ng HKT, Kundu D, Balakrishnan N. Modified moment estimation for the two-parameter Birnbaum-Saunders distribution. Comput. Stat. Data Anal. 2003;43:283-298. doi: 10.1016/S0167-9473(02)00254-2[Crossref], [Web of Science ®], [Google Scholar] · Zbl 1429.62451
[8] Jeng S-L. Inference for the fatigue life model based on the Birnbaum-Saunders distribution. Commun. Stat. Simul. Comput. 2003;32:43-60. doi: 10.1081/SAC-120013110[Taylor & Francis Online], [Web of Science ®], [Google Scholar] · Zbl 1100.62512
[9] Wu J, Wong ACM. Improved interval estimation for the two-parameter Birnbaum-Saunders distribution. Comput. Statist. Data Anal. 2004;47:809-821. doi: 10.1016/j.csda.2003.11.018[Crossref], [Web of Science ®], [Google Scholar] · Zbl 1429.62103
[10] Ng HKT, Kundu D, Balakrishnan N. Point and interval estimation for the two-parameter Birnbaum Saunders distribution based on Type-II censored samples. Comput. Statist. Data Anal. 2006;50:3222-3242. doi: 10.1016/j.csda.2005.06.002[Crossref], [Web of Science ®], [Google Scholar] · Zbl 1161.62418
[11] Lemonte AJ, Neto FC, Vasconcellos KLP. Improved statistical inference for the two-parameter Birnbaum-Saunders distribution. Comput. Statist. Data Anal. 2007;51:4656-4681. doi: 10.1016/j.csda.2006.08.016[Crossref], [Web of Science ®], [Google Scholar] · Zbl 1162.62419
[12] Wang BX. Generalized interval estimation for the Birnbaum-Saunders distribution. Comput. Statist. Data Anal. 2012;56:4320-4326. doi: 10.1016/j.csda.2012.03.023[Crossref], [Web of Science ®], [Google Scholar] · Zbl 1255.62077
[13] Achcar JA. Inferences for the Birnbaum Saunders fatigue life model using Bayesian methods. Comput. Statist. Data Anal. 1993;15:367-380. doi: 10.1016/0167-9473(93)90170-X[Crossref], [Web of Science ®], [Google Scholar] · Zbl 0875.62462
[14] Xu A, Tang Y. Reference analysis for Birnbaum-Saunders distribution. Comput. Statist. Data Anal. 2010;54:185-192. doi: 10.1016/j.csda.2009.08.004[Crossref], [Web of Science ®], [Google Scholar] · Zbl 1284.62186
[15] Xu A, Tang Y. Bayesian analysis of Birnbaum-Saunders distribution with partial information. Comput. Statist. Data Anal. 2011;55:2324-2333. doi: 10.1016/j.csda.2011.01.021[Crossref], [Web of Science ®], [Google Scholar] · Zbl 1328.62181
[16] Fisher RA. Inverse probability. Proc. Camb. Philos. Soc. 1930;xxvi:528-535. doi: 10.1017/S0305004100016297[Crossref], [Google Scholar] · JFM 56.1083.05
[17] Zabell SL. R.A. Fisher and the fiducial argument. Stat. Sci. 1992;7:369-387. doi: 10.1214/ss/1177011233[Crossref], [Google Scholar] · Zbl 0955.62521
[18] Weeranhandi S. Generalized confidence intervals. J. Am. Stat. Assoc. 1993;88:899-905. doi: 10.1080/01621459.1993.10476355[Taylor & Francis Online], [Web of Science ®], [Google Scholar] · Zbl 0785.62029
[19] Hannig J, Iyer HK, Patterson P. Fiducial generalized confidence intervals. J. Am. Stat. Assoc. 2006;101:254-269. doi: 10.1198/016214505000000736[Taylor & Francis Online], [Web of Science ®], [Google Scholar] · Zbl 1118.62316
[20] Hannig J. On generalized fiducial inference. Stat. Sinica. 2009;19:491-544. [Web of Science ®], [Google Scholar] · Zbl 1168.62004
[21] Wandler DV, Hannig J. Fiducial inference on the largest mean of a multivariate normal distribution. J Multivar. Anal. 2011;102:87-104. doi: 10.1016/j.jmva.2010.08.003[Crossref], [Web of Science ®], [Google Scholar] · Zbl 1206.62112
[22] Wandler DV, Hannig J. Generalized fiducial confidence intervals for extremes. Extremes. 2012;15:67-87. doi: 10.1007/s10687-011-0127-9[Crossref], [Web of Science ®], [Google Scholar] · Zbl 1329.62237
[23] Wang CM, Hannig J, Iyer HK. Fiducial prediction intervals. J Stat. Plan. Infer. 2012;142:1980-1990. doi: 10.1016/j.jspi.2012.02.021[Crossref], [Web of Science ®], [Google Scholar] · Zbl 1237.62038
[24] Hannig J. Generalized fiducial inference via discretization. Stati. Sinica. 2013;23:489-514. [Web of Science ®], [Google Scholar] · Zbl 1379.62002
[25] Sun ZL. The confidence intervals for the scale parameter of the Birnbaum-Saunders fatigue life distribution. Chin. J. Acta Armament. 2009;33:51-60. [Google Scholar]
[26] Gilks WR, Wild P. Adaptive rejection sampling for Gibbs sampling. Appl. Stat. 1992;41(2):337-348. doi: 10.2307/2347565[Crossref], [Web of Science ®], [Google Scholar] · Zbl 0825.62407
[27] Gilks WR, Best NG, Tan KKC. Adaptive rejection metropolis sampling within Gibbs sampling. Appl. Stat. 1995;44:455-472. doi: 10.2307/2986138[Crossref], [Web of Science ®], [Google Scholar] · Zbl 0893.62110
[28] Petris G. dlm: an R package for Bayesian analysis of dynamic linear models. Fayetteville AR: University of Arkansas; 2009. [Google Scholar]
[29] Ihaka R, Gentleman R. IR: a language for data analysis and graphics. J. Comput. Grap. Stat. 1996;5(3):299-314. [Taylor & Francis Online], [Google Scholar]
[30] Byrd RH, Lu P, Nocedal J, Zhu C. A limited memory algorithm for bound constrained optimization. SIAM J Scient. Comput. 1995;16:1190-1208. doi: 10.1137/0916069[Crossref], [Web of Science ®], [Google Scholar] · Zbl 0836.65080
[31] McCool JI. IInferential techniques for Weibull populations. Aerospace Research Laboratories ReportARL TR74-0180, Wright-Patterson Air Force Base, Dayton, OH; 1974. [Google Scholar] · Zbl 0305.62066
[32] Cohen AC, Whitten BJ, Ding Y. Modified momente stimation for the three-parameter Weibull distribution. J. Qual. Technol. 1984;16:159-167. [Taylor & Francis Online], [Web of Science ®], [Google Scholar]
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.