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Prediction Intervals for Proportional Hazard Rate Models Based on Progressively Type II Censored Samples
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 Title & Authors
Prediction Intervals for Proportional Hazard Rate Models Based on Progressively Type II Censored Samples
Asgharzadeh, A.; Valiollahi, R.;
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 Abstract
In this paper, we present two methods for obtaining prediction intervals for the times to failure of units censored in multiple stages in a progressively censored sample from proportional hazard rate models. A numerical example and a Monte Carlo simulation study are presented to illustrate the prediction methods.
 Keywords
Progressive Type-II censoring;proportional hazard rate model;prediction interval;highest conditional density;Monte Carlo simulation;
 Language
English
 Cited by
1.
Predictions for Progressively Type-II Censored Failure Times from the Half Triangle Distribution,;;

Communications for Statistical Applications and Methods, 2014. vol.21. 1, pp.93-103 crossref(new window)
1.
Predictions for Progressively Type-II Censored Failure Times from the Half Triangle Distribution, Communications for Statistical Applications and Methods, 2014, 21, 1, 93  crossref(new windwow)
2.
Bayesian Prediction for Progressive Censored Data From the Weibull-Geometric Model, American Journal of Mathematical and Management Sciences, 2017, 36, 3, 247  crossref(new windwow)
3.
Prediction of Future Failures Times based on Type-I Hybrid Censored Samples of Random Sample Sizes, Communications in Statistics - Simulation and Computation, 2017, 0  crossref(new windwow)
 References
1.
Ahmadi, J., Jafari Jozani, M., Marchand, E. and Parsian, A. (2009a). Prediction of k-records from a general class of distributions under balanced type loss functions, Metrika, 70, 19-33. crossref(new window)

2.
Ahmadi, J., Jafari Jozani, M., Marchand, E. and Parsian, A. (2009b). Bayesian estimation based on k-record data from a general class of distributions under balanced type loss functions, Journal of Statistical Planning and Inference, 139, 1180-1189. crossref(new window)

3.
Awad, A. M. and Raqab, M. Z. (2000). Prediction intervals for the future record values from exponential distribution: Comparative study, Journal of Statistical Computation and Simulation, 65, 325-340. crossref(new window)

4.
Balakrishnan, N. (2007). Progressive censoring methodology: An appraisal, Test, 16, 211-259. crossref(new window)

5.
Balakrishnan, N. and Lin, C. T. (2002). Exact linear inference and prediction for exponential distributions based on general progressively Type-II censored samples, Journal of Statistical Computation and Simulation, 72, 677-686. crossref(new window)

6.
Basak, I., Basak, P. and Balakrishnan, N. (2006). On some predictors of times to failure of censored items in progressively censored samples, Computational Statistics & Data Analysis, 50, 1313-1337. crossref(new window)

7.
Marshall, A. W. and Olkin, I. (2007). Life Distributions, Springer, New York.

8.
Nelson, W. (1982). Applied Life Data Analysis, John Wiley & Sons, New York.

9.
Ng, H. K. T., Chan, P. S. and Balakrishnan, N. (2004). Optimal progressive censoring plans for the Weibull distribution, Technometrics, 46, 470-481. crossref(new window)

10.
Raqab, M. Z. and Nagaraja, H. N. (1995). On some predictors of future order statistics, Metron, 53, 185-204.

11.
Viveros, R. and Balakrishnan, N. (1994). Interval estimation of life characteristics from progressively censored data, Technometrics, 36, 84-91. crossref(new window)