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Annals of Data Science

Erschienen in:

29.05.2020

Inference for Kumaraswamy Distribution Based on Type I Progressive Hybrid Censoring

verfasst von: Farha Sultana, Yogesh Mani Tripathi, Shuo-Jye Wu, Tanmay Sen

Erschienen in: Annals of Data Science | Ausgabe 6/2022

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Abstract

In this paper, we investigate the estimation problems of unknown parameters of the Kumaraswamy distribution under type I progressive hybrid censoring. This censoring scheme is a combination of progressive type I and hybrid censoring schemes. We derive the maximum likelihood estimates of parameters using an expectation-maximization algorithm. Bayes estimates are obtained under different loss functions using the Lindley method and importance sampling procedure. The highest posterior density intervals of unknown parameters are constructed as well. We also obtain prediction estimates and prediction intervals for censored observations. A Monte Carlo simulation study is performed to compare proposed methods and one real data set is analyzed for illustrative purposes.

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Aban IB, Meerschaert MM, Panorska AK (2006) Parameter estimation for the truncated pareto distribution. J Am Stat Assoc 101:270–277CrossRef

Ahmed SO, Reyad HM (2016) Bayesian and E-Bayesian estimation for the Kumaraswamy distribution based on type-II censoring. Int J Adv Math Sci 4:10–17

Alma OG, Belaghi RA (2016) On the estimation of the extreme value and normal distribution parameters based on progressive type-II hybrid censored data. J Stat Comput Simul 86:569–596CrossRef

Balakrishnan N, Aggarwala R (2000) Progressive censoring - theory, methods, and applications. Birkhäuser, BostonCrossRef

Balakrishnan N, Cramer E (2014) The art of progressive censoring: applications to reliability and quality. Birkhäuser, BostonCrossRef

Balakrishnan N, Kateri M (2008) On the maximum likelihood estimation of parameters of Weibull distribution based on complete and censored data. Stat Prob Lett 78:2971–2975CrossRef

Balakrishnan N, Kundu D (2013) Hybrid censoring: models, inferential results and applications. Comput Stat Data Anal 57:166–209CrossRef

Chen M-H, Shao Q-M (1999) Monte Carlo estimation of Bayesian credible and HPD intervals. J Comput Gr Stat 8:69–92

Childs A, Chandrasekar B, Balakrishnan N (2008) Exact likelihood inference for an exponential parameter under progressive hybrid censoring. In: Vonta F, Nikulin M, Limnios N, Huber-Carol C (eds) Statistical models and methods for biomedical and technical systems. Birkhäuser, Boston, pp 319–330CrossRef

10.

Childs A, Chandrasekar B, Balakrishnan N, Kundu D (2003) Exact likelihood inference based on type-I and type-II hybrid censored samples from the exponential distribution. Ann Inst Stat Math 55:319–330CrossRef

11.

Cordeiro GM, Nadarajah S, Ortega EMM (2012) The Kumaraswamy Gumbel distribution. Stat Methods Appl 21:139–168CrossRef

12.

Cordeiro GM, Ortega EMM, Silva GO (2014) The Kumaraswamy modified Weibull distribution: theory and applications. J Stat Comput Simul 84:1387–1411CrossRef

13.

Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B 39:1–38

14.

Dey S, Mazucheli J, Anis MZ (2017) Estimation of reliability of multicomponent stress-strength for a Kumaraswamy distribution. Commun Stat Theory Methods 46:1560–1572CrossRef

15.

Eldin MM, Khalil N, Amein M (2014) Estimation of parameters of the Kumaraswamy distribution based on general progressive type II censoring. Am J Theor Appl Stat 3:217–222CrossRef

16.

Elgarhy M, Haq MAu, ul Ain Q (2018) Exponentiated generalized Kumaraswamy distribution with applications. Ann Data Sci 5:273–292CrossRef

17.

El-Sherpieny EA, Elsehetry MM (2019) Type II Kumaraswamy half logistic family of distributions with applications to exponential model. Ann Data Sci 6:1–20CrossRef

18.

Epstein B (1954) Truncated life tests in the exponential case. Ann Math Stat 25:555–564CrossRef

19.

Ganji A, Ponnambalam K, Khalili D, Karamouz M (2006) Grain yield reliability analysis with crop water demand uncertainty. Stoch Environ Res Risk Assess 20:259–277CrossRef

20.

Hemmati F, Khorram E (2013) Statistical analysis of the log-normal distribution under type-II progressive hybrid censoring schemes. Commun Stat Simul Comput 42:52–75CrossRef

21.

Jones MC (2009) Kumaraswamys distribution: a beta-type distribution with some tractability advantages. Stat Methodol 6:70–81CrossRef

22.

Kayal T, Tripathi YM, Rastogi MK, Asgharzadeh A (2017) Inference for Burr XII distribution under type I progressive hybrid censoring. Commun Stat Simul Comput 46:7447–7465CrossRef

23.

Kernane T, Raizah ZA (2009) Fixed point iteration for estimating the parameters of extreme value distributions. Commun Stat Simul Comput 38:2161–2170CrossRef

24.

Kumaraswamy P (1980) A generalized probability density function for double-bounded random processes. J Hydrol 46:79–88CrossRef

25.

Kundu D, Joarder A (2006) Analysis of type-II progressively hybrid censored data. Comput Stat Data Anal 50:2509–2528CrossRef

26.

Kundu D, Pradhan B (2009) Bayesian inference and life testing plans for generalized exponential distribution. Sci China Ser A Math 52:1373–1388CrossRef

27.

Kizilaslan F, Nadar M (2018) Estimation of reliability in a multicomponent stress-strength model based on a bivariate Kumaraswamy distribution. Stat Pap 59:307–340CrossRef

28.

Lin C-T, Chou C-C, Huang Y-L (2012) Inference for the Weibull distribution with progressive hybrid censoring. Comput Stat Data Anal 56:451–467CrossRef

29.

Lin C, Huang Y (2012) On progressive hybrid censored exponential distribution. J Stat Comput Simul 82:689–709CrossRef

30.

Lindley DV (1980) Approximate Bayesian method. Trabajos de Estadistica 31:223–237CrossRef

31.

Mitnik PA (2013) New properties of the Kumaraswamy distribution. Commun Stat Theory Methods 42:741–755CrossRef

32.

Nadar M, Papadopoulos A, Kizilaslan F (2013) Statistical analysis for Kumaraswamy’s distribution based on record data. Stat Pap 54:355–369CrossRef

33.

Nadarajah S (2008) On the distribution of Kumaraswamy. J Hydrol 348:568–569CrossRef

34.

Olson D, Shi D (2007) Introduction to business data mining. McGraw-Hill, Irwin

35.

Park S, Balakrishnan N, Kim SW (2011) Fisher information in progressive hybrid censoring schemes. Statistics 45:623–631CrossRef

36.

Tomer SK, Panwar MS (2015) Estimation procedures for Maxwell distribution under type-I progressive hybrid censoring scheme. J Stat Comput Simul 85:339–356CrossRef

37.

Seifi A, Ponnambalam K, Vlach J (2000) Maximization of manufacturing yield of systems with arbitrary distributions of component values. Ann Oper Res 99:373–383CrossRef

38.

Sindhu TN, Feroze N, Aslam M (2013) Bayesian analysis of the Kumaraswamy distribution under failure censoring sampling scheme. Int J Adv Sci Technol 51:39–58

39.

Sultana F, Tripathi YM, Rastogi MK, Wu S-J (2018) Parameter estimation for the Kumaraswamy distribution based on hybrid censoring. Am J Math Manag Sci 37:243–261

40.

Wang BX, Wang XK, Yu K (2017) Inference on the Kumaraswamy distribution. Commun Stat Theory Methods 46:2079–2090CrossRef

41.

Shi Y (2014) Big data: history, current status, and challenges going forward. Bridge US Natl Acad Eng 44:6–11

42.

Zhang T, Xie M (2011) On the upper truncated Weibull distribution and its reliability implications. Reliab Eng Syst Saf 96:194–200CrossRef

43.

Zellner A (1986) Bayesian estimation and prediction using asymmetric loss functions. J Am Stat Assoc 81:446–451CrossRef

Titel: Inference for Kumaraswamy Distribution Based on Type I Progressive Hybrid Censoring
verfasst von: Farha Sultana
Yogesh Mani Tripathi
Shuo-Jye Wu
Tanmay Sen
Publikationsdatum: 29.05.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Annals of Data Science / Ausgabe 6/2022
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI: https://doi.org/10.1007/s40745-020-00283-z

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