Type-II stepwise progressive censoring

Title & Authors
Type-II stepwise progressive censoring

Abstract
Type-II progressive censoring is one of the censoring methods frequently used in clinical studies, reliability trials, quality control of products and industrial experiments. Sometimes in Type-II progressive censoring experiments, the failure rate is low so the waiting time to observe the $\small{m^{th}}$ failure will be very long; however, the experimenter may have to terminate the experiment before a predetermined time. In this article, if two generalized types of Type-II progressive censoring are reminded, we then make some changes in the removal method of Type-II progressive censoring such that without reducing the deduction quality, the termination time of the experiment decreases. This can be done with decreasing withdraws throughout the steps of the experiment with a special reasonable method. A simulation study is done and the results are tabulated at the end of this article for a comparison between introduced method and Type-II progressive censoring.
Keywords
Type-II stepwise progressive censoring;Type-II progressive censoring;maximum likelihood estimator;lifetime experiment;failure rate;test duration;
Language
English
Cited by
References
1.
Bairamov I and Parsi S (2011). On flexible progressive censoring, Journal of Computational and Applied Mathematics, 235, 4537-4544.

2.
Balakrishnan N (2007). Progressive censoring methodology: an appraisal (with discussions), Test, 16, 211-296.

3.
Balakrishnan N and Aggarwala R (2000). Progressive Censoring: Theory, Methods, and Applications, Birkhauser, Boston.

4.
Balakrishnan N, Burkschat M, Cramer E, and Hofmann G (2008). Fisher information based progres-sive censoring plans, Computational Statistics and Data Analysis, 53, 366-380.

5.
Balakrishnan N and Cramer E (2014). The Art of Progressive Censoring, Springer, New York.

6.
Balakrishnan N, Cramer E, and Iliopoulos G (2014). On the method of pivoting the CDF for exact confidence intervals with illustration for exponential mean under life-test with time constraints, Statistics and Probability Letters, 89, 124-130.

7.
Burkschat M (2008). On optimality of extremal schemes in progressive Type-II censoring, Journal of Statistical Planning and Inference, 138, 1647-1659.

8.
Burkschat M, Cramer E, and Kamps U (2006). On optimal schemes in progressive censoring, Statistics and Probability Letters, 76, 1032-1036.

9.
Caroni C (2002). The correct ball bearings data, Lifetime Data Anal, 8, 395-399.

10.
Cohen AC (1963). Progressively censored samples in life testing, Technometrics, 5, 327-329.

11.
Cramer E (2014). Extreme value analysis for progressively Type-II censored order statistics, Communications in Statistics-Theory and Methods, 43, 2135-2155.

12.
Cramer E and Iliopoulos G (2009). Adaptive progressive Type-II censoring, Test, 19, 342-358.

13.
Cramer E and Kamps U (2001). Estimation with sequential order statistics from exponential distributions, Annals of the Institute of Statistical Mathematics, 53, 307-324.

14.
Dey S and Dey T (2014). Statistical inference for the Rayleigh distribution under progressively Type-II censoring with binomial removal, Applied Mathematical Modelling, 38, 974-982.

15.
Ghitany ME, Al-Jarallah RA, and Balakrishnan N (2013). On the existence and uniqueness of the MLEs of the parameters of a general class of exponentiated distributions, Statistics, 47, 605-612.

16.
Ghitany ME, Tuan VK, and Balakrishnan N (2014). Likelihood estimation for a general class of inverse exponentiated distributions based on complete and progressively censored data, Journal of Statistical Computation and Simulation, 84, 96-106.

17.
Herd RG (1956). Estimation of parameters of a population from a multi-Censored Sample, Phd Thesis, Iowa State College, Ames, Iowa.

18.
Kamps U and Cramer E (2001). On distributions of generalized order statistics, Statistics, 35, 269-280.

19.
Kang SB and Seo JI (2011). Estimation in an exponentiated half logistic distribution under progres-sively Type-II censoring, Communications for Statistical Applications and Methods, 18, 657-366.

20.
Kinaci I (2013). A generalization of flexible progressive censoring, Pakistan Journal of Statistics, 29, 377-387.

21.
Krishna H and Kumar K (2013). Reliability estimation in generalized inverted exponential distribution with progressively Type II censored sample, Journal of Statistical Computation and Simulation, 83, 1007-1019.

22.
Lieblein J and ZelenM(1956). Statistical investigation of the fatigue life of deep-groove ball bearings, Journal of Research of the National Bureau of Standards, 57, 273-316.

23.
Ng HKT, Kundu D, and Chan PS (2009). Statistical of analysis of exponential lifetimes under an adaptive Type-II progressive censoring scheme, Naval Research logistics, 56, 687-698.

24.
Pakyari R and Balakrishnan N (2013). Goodness-of-fit tests for progressively Type-II censored data from location-scale distributions, Journal of Statistical Computation and Simulation, 83, 167-178.

25.
Raqab MZ (2002). Inference for generalized exponential distribution based on record statistics, Journal of Statistical Planning and Inference, 104, 339-350.

26.
Rezapour M, Alamatsaz MH, and Balakrishnan N (2013a). On properties of dependent progressively Type-II censored order statistics, Metrika, 76, 909-917.

27.
Rezapour M, Alamatsaz MH, Balakrishnan N, and Cramer E (2013b). On properties of progressively Type-II censored order statistics arising from dependent and nonidentical random variables, Statistical Methodology, 10, 58-71.

28.
Sarhan AM and Al-Ruzaizaa A (2010). Statistical inference in connection with the Weibull model using Type-II progressively censored data with random scheme, Pakistan Journal of Statistics, 26, 267-279.

29.
Seo JI and Kang SB (2014). Predictions for progressively Type-II censored failure times from the half triangle distribution, Communications for Statistical Applications and Methods, 21, 93-103.

30.
Tse SK, Yang C, and Yuen HK (2000). Statistical analysis of Weibull distributed lifetime data under Type II progressive censoring with binomial removals, Journal of Applied Statistics, 27, 1033-1043.