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Inference and expected total test time for step-stress life test in the presence of complementary risks and incomplete data

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Abstract

The complementary risk is common and important in the engineering field. However, there is not much research about it because of its complex derivation compared with the competing risk model. In this paper, we concentrate on inference of step-stress partially accelerated life test in the presence of complementary risks under progressive type-II censoring scheme. The Weibull distribution is chosen as the baseline lifetime of the model. The tampered random variable model is adopted as the statistical acceleration model in the accelerated test. We apply both the classical and Bayesian methods to obtain the estimation of lifetime parameters and acceleration factors. The reliability and reversed hazard rate are estimated based on the parametric estimates. The computational formulae of expected total test time are creatively derived under the step-stress and censored setting. The theoretical calculations are compared with simulated values to verify the derivation. Also, numerical studies including the simulation study and real-data analysis in engineering background are conducted to compare and illustrate the performance of the approaches proposed in the paper. Some conclusions and suggestions for actual production are given at the end of the paper.

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References

  • Aljohani HM, Alfar NM (2020) Estimations with step-stress partially accelerated life tests for competing risks Burr XII lifetime model under type-II censored data. Alex Eng J 59:1171–1180

    Article  Google Scholar 

  • Balakrishnan N, Aggarwala R (2000) Progressive censoring: theory, methods, and applications. Springer, New York

    Book  Google Scholar 

  • Balakrishnan N, Sandhu RA (1995) A simple simulational algorithm for generating progressive type-II censored samples. Am Stat 49(2):229–230

    Article  Google Scholar 

  • Basu AP, Klein JP (1982), ‘Some recent results in competing risks theory. IMS Lecture Notes Monogr. Ser.: Survival Analysis. 2: 216–229

  • Block HW, Savits TH, Singh H (1998) The reversed hazard rate function. Probab Eng Inf Sci 12(1):69–90

    Article  MathSciNet  Google Scholar 

  • Chacko M, Mohan R (2019) Bayesian analysis of Weibull distribution based on progressive type-II censored competing risks data with binomial removals. Comput Stat 34:233–252

    Article  MathSciNet  Google Scholar 

  • Ghaly AAA, Aly HM, Salah RN (2020) Applying the copula approach on step stress accelerated life test under type II censoring. Commun Stat Simul Comput 49(1):159–177

    Article  MathSciNet  Google Scholar 

  • Goel PK (1971) Some estimation problems in the study of tampered random variables. Technical Report no. 50, Department of Statistics, Carnegie-Mellon University, Pittsburgh, Pennsylvania

  • Greene WH (2000) Econometric analysis, 4th edn. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Gunasekera S (2018) Inference for the reliability function based on progressively type II censored data from the Pareto model: the generalized variable approach. J Comput Appl Math 343:275–288

    Article  MathSciNet  Google Scholar 

  • Han D (2015) Estimation in step-stress life tests with complementary risks from the exponentiated exponential distribution under time constraint and its applications to UAV data. Stat Methodol 23:103–122

    Article  MathSciNet  Google Scholar 

  • Ismail AA (2014) Inference for a step-stress partially accelerated life test model with an adaptive type-II progressively hybrid censored data from Weibull distribution. J Comput Appl Math 260:533–542

    Article  MathSciNet  Google Scholar 

  • Ismail AA (2020) Theoretical aspects of the development of partially accelerated life testing using Bayesian estimation. Int J Fatigue 134:105459

    Article  Google Scholar 

  • Kamps U, Cramer E (2001) On distributions of generalized order statistics. Statistics 35(3):269–280

    Article  MathSciNet  Google Scholar 

  • Koley A, Kundu D (2021) Analysis of progressive type-II censoring in presence of competing risk data under step stress modeling. Stat Neerl 75:115–136

    Article  MathSciNet  Google Scholar 

  • Kotb MS, Raqad MZ (2019) Inference for a simple step-stress model based on ordered ranked set sampling. Appl Math Model 75:23–36

    Article  MathSciNet  Google Scholar 

  • Kundu D, Ganguly A (2017) Analysis of step-stress models. Academic Press, London

    Google Scholar 

  • Li J, Tian Y, Wang D (2020) Change-point detection of failure mechanism for electronic devices based on Arrhenius model. Appl Math Model 83:46–58

    Article  MathSciNet  Google Scholar 

  • Liu F, Shi Y (2017) Inference for a simple step-stress model with progressively censored competing risks data from Weibull distribution. Commun Stat Theory Methods 46:7238–7255

    Article  ADS  MathSciNet  Google Scholar 

  • Meeker WQ, Escobar L (1998) Statistical methods for reliability data. Wiley, New York

    Google Scholar 

  • Ng HKT, Kundu D, Chan P (2009) Statistical analysis of exponential lifetimes under an adaptive type-II progressive censoring scheme. Nav Res Logist 56:687–698

    Article  MathSciNet  Google Scholar 

  • Pakyari R, Baklizi A (2022) On goodness-of-fit testing for Burr type X distribution under progressively type-II censoring. Comput Stat. https://doi.org/10.1007/s00180-022-01197-5

    Article  MathSciNet  Google Scholar 

  • Qiu Q, Cui L (2018) Reliability evaluation based on a dependent two-stage failure process with competing failures. Appl Math Model 64:699–712

    Article  MathSciNet  Google Scholar 

  • Samanta D, Ganguly A, Gupta A, Kundu D (2019) On classical and bayesian order restricted inference for multiple exponential step stress model. Statistics 53(1):177–195

    Article  MathSciNet  Google Scholar 

  • Sultana F, Dewanji A (2021) Tampered random variable modeling for multiple step-stress life test. Commun Stat Theory Methods. https://doi.org/10.1080/03610926.2021.2008440

    Article  Google Scholar 

  • Tian Y, Gui W (2021) Inference of weighted exponential distribution under progressively type-II censored competing risks model with electrodes data. J Stat Comput Simul 91(16):3426–3452

    Article  MathSciNet  Google Scholar 

  • Tian Y, Gui W (2022) Statistical inference of dependent competing risks from Marshall-Olkin bivariate Burr-XII distribution under complex censoring. Commun Stat Simul Comput. https://doi.org/10.1080/03610918.2022.2093373

    Article  Google Scholar 

  • Wu M, Shi Y (2016) Bayes estimation and expected termination time for the competing risks model from Gompertz distribution under progressively hybrid censoring with binomial removals. J Comput Appl Math 300:420–431

    Article  MathSciNet  Google Scholar 

Download references

Funding

This work was partially supported by The Development Project of China Railway (No. N2022J017) and the Fund of China Academy of Railway Sciences Corporation Limited (No. 2022YJ161).

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Correspondence to Wenhao Gui.

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Appendices

Appendix

A Elements of observed Fisher information matrix \(I(\hat{\theta })\)

The second partial derivative of information matrix can be calculated as

$$\begin{aligned}&\frac{\partial ^{2} \ln L}{\partial \alpha _{j}^{2}}=-\frac{n_{1j}+n_{2j}}{\alpha _{j}^{2}} + \sum _{k=1}^{N_{1}} I({\delta }_{k}=j) \left[ -\lambda _{j} x_{k}^{\alpha _{j}}\left( \ln x_{k}\right) ^{2}\right] + \sum _{k=N_{1}+1}^{m} I({\delta }_{k}=j) \left[ -\lambda _{j} z_{k}^{\alpha _{j}}\left( \ln z_{k}\right) ^{2}\right] \\&+ \sum _{k=1}^{N_{1}} R_{k} \lambda _{j} x_{k}^{\alpha _j} \left[ \left( \ln x_{k}\right) ^{2} B_{kj(x)}+\ln x_{k} B'_{kj(x)1} \right] + \sum _{k=N_{1}+1}^{m} R_{k} \lambda _{j} z_{k}^{\alpha _j} \left[ \left( \ln z_{k}\right) ^{2} B_{kj(z)}+\ln z_{k} B'_{kj(z)1} \right] \\&+ \sum _{k=1}^{N_{1}} I({\delta }_{k}=3-j) \lambda _{j} x_{k}^{\alpha _{j}} \left[ \left( \ln x_{k}\right) ^{2}A_{kj(x)}+\ln x_k A'_{kj(x)1} \right] \\&+ \sum _{k=N_{1}+1}^{m} I({\delta }_{k}=3-j) \lambda _{j} z_{k}^{\alpha _{j}} \left[ \left( \ln z_{k}\right) ^{2}A_{kj(z)}+ \ln z_k A'_{kj(z)1} \right] , \\&\frac{\partial ^{2} \ln L}{\partial \lambda _{j}^{2}} = -\frac{n_{1j}+n_{2j}}{\lambda _{j}^{2}} + \sum _{k=1}^{N_{1}} I({\delta }_{k}=3-j) x_{k}^{\alpha _{j}} A'_{kj(x)2} + \sum _{k=1}^{N_{1}} R_{k} x_{k}^{\alpha _{j}} B'_{kj(x)2} \\&+ \sum _{k=1}^{N_{1}} I({\delta }_{k}=3-j) z_{k}^{\alpha _{j}} A'_{kj(z)2} + \sum _{k=1}^{N_{1}} R_{k} z_{k}^{\alpha _{j}} B'_{kj(z)2},\\&\frac{\partial ^{2} \ln L}{\partial \alpha _{j} \partial \lambda _{j}}=\frac{\partial ^{2} \ln L}{\partial \lambda _{j} \partial \alpha _{j} } = - \sum _{k=1}^{N_{1}} I({\delta }_{k}=j) x_{k}^{\alpha _j} \ln x_k + \sum _{k=1}^{N_{1}} I({\delta }_{k}=3-j) x_{k}^{\alpha _j} \left[ \ln x_k A_{kj(x)}+A'_{kj(x)2} \right] \\&- \sum _{k=N_{1}+1}^{m} I({\delta }_{k}=j) z_{k}^{\alpha _j} \ln z_k + \sum _{k=N_{1}+1}^{m} I({\delta }_{k}=3-j) z_{k}^{\alpha _j} \left[ \ln z_k A_{kj(z)}+A'_{kj(z)2} \right] \\&+ \sum _{k=1}^{N_{1}} R_{k} x_{k}^{\alpha _j} \left[ \ln x_k B_{kj(x)} + B'_{kj(x)2} \right] + \sum _{k=N_{1}+1}^{m} R_{k} z_{k}^{\alpha _j} \left[ \ln z_k B_{kj(z)} + B'_{kj(z)2} \right] , \\&\frac{\partial ^{2} \ln L}{\partial \alpha _{j} \partial \beta _{j}}=\frac{\partial ^{2} \ln L}{\partial \beta _{j} \partial \alpha _{j} } = \sum _{k=N_{1}+1}^{m} I({\delta }_{k}=j) \left[ \frac{z'_{kj}}{z_{kj}} - \lambda _{j}(\alpha _{j} z_{k}^{\alpha _j-1} z'_{kj} \ln z_{kj} + z_{k}^{\alpha _j} \frac{z'_{kj}}{z_{kj}}) \right] \\&+ \sum _{k=N_{1}+1}^{m} I({\delta }_{k}=3-j) \lambda _j \left[ \alpha _{j} z_{k}^{\alpha _j-1} z'_{kj} \ln z_{kj} A_{kj(z)} + z_{k}^{\alpha _j} \frac{z'_{kj}}{z_{kj}} A_{kj(z)} + z_{k}^{\alpha _j} \ln z_{kj} A'_{kj(z)3} \right] \\&+ \sum _{k=N_{1}+1}^{m} R_k \lambda _j \left[ \alpha _{j} z_{k}^{\alpha _j-1} z'_{kj} \ln z_{kj} B_{kj(z)} + z_{k}^{\alpha _j} \frac{z'_{kj}}{z_{kj}} B_{kj(z)} + z_{k}^{\alpha _j} \ln z_{kj} B'_{kj(z)3} \right] , \\&\frac{\partial ^{2} \ln L}{\partial \lambda _{j} \partial \beta _{j}}=\frac{\partial ^{2} \ln L}{\partial \beta _{j} \partial \lambda _{j} } = \sum _{k=N_{1}+1}^{m} I({\delta }_{k}=3-j) \left[ \alpha _{j} z_{k}^{\alpha _j-1} z'_{kj} A_{kj(z)} + z_{k}^{\alpha _j} A'_{kj(z)3} \right] \\&-\sum _{k=N_{1}+1}^{m} I({\delta }_{k}=j) \alpha _{j} z_{k}^{\alpha _j-1} z'_{kj} + \sum _{k=N_{1}+1}^{m} R_k \left[ \alpha _{j} z_{k}^{\alpha _j-1} z'_{kj} B_{kj(z)} + z_{k}^{\alpha _j} B'_{kj(z)3} \right] , \\&\frac{\partial ^{2} \ln L}{\partial \beta _{j}^{2}} = \frac{n_{2j}}{\beta _{j}^{2}} + \sum _{k=N_{1}+1}^{m} I({\delta }_{k}=j) \left[ \frac{\alpha _j-1}{\alpha _j \lambda _j} (\frac{z'_{kj}}{z_{kj}^{2}}C_{kj(x)}-\frac{C'_{kj(x)}}{z_{kj}}) - z_{k}^{\alpha _j-1}C'_{kj(x)} - (\alpha _j-1)z_{k}^{\alpha _j-2}z'_{kj}C_{kj(x)} \right] \\&- \sum _{k=N_{1}+1}^{m} I({\delta }_{k}=3-j) \left[ A'_{kj(z)3}C_{kj(x)}z_{k}^{\alpha _j-1} + A_{kj(z)}C'_{kj(x)}z_{k}^{\alpha _j-1} + (\alpha _j-1)A_{kj(z)}C_{kj(x)}z_{k}^{\alpha _j-2}z'_{kj} \right] \\&+ \sum _{k=N_{1}+1}^{m} R_k \left[ B'_{kj(z)3}C_{kj(x)}z_{k}^{\alpha _j-1} + B_{kj(z)}C'_{kj(x)}z_{k}^{\alpha _j-1} + (\alpha _j-1)B_{kj(z)}C_{kj(x)}z_{k}^{\alpha _j-2}z'_{kj} \right] , \end{aligned}$$

where \(A'_{kj(x)o}\), \(A'_{kj(z)o}\), \(B'_{kj(x)o}\) and \(B'_{kj(z)o}\) (\(o=1,2,3\)) are the derivatives of \(A_{kj(x)}\), \(A_{kj(z)}\), \(B_{kj(x)}\) and \(B_{kj(z)}\) over \(\alpha _j\), \(\lambda _j\) and \(\beta _j\), respectively. \(C'_{kj(x)}\) and \(z'_{kj}\) are the derivatives of \(C_{kj(x)}\) and \(z_{kj}\) over \(\beta _j\).

B Elements of derivative vectors \(D_1\) and \(D_2\)

The first partial derivative vectors of R(x) and r(x) can be calculated as

$$\begin{aligned}{} & {} \dfrac{\partial R(x)}{\partial \alpha _{j}} = -\lambda _{j}(1-e^{-\lambda _{3-j}y^{\alpha _{3-j}}}) e^{-\lambda _{j}y^{\alpha _{j}}} y^{\alpha _{j}} \log y, \quad \dfrac{\partial R(x)}{\partial \lambda _{j}} = -(1-e^{-\lambda _{3-j}y^{\alpha _{3-j}}}) e^{-\lambda _{j}y^{\alpha _{j}}} y^{\alpha _{j}},\\{} & {} \quad \dfrac{\partial R(x)}{\partial \beta _{j}} = \alpha _j \lambda _j (1-e^{-\lambda _{3-j}z^{\alpha _{3-j}}}) e^{-\lambda _{j}z^{\alpha _{j}}} z^{\alpha _{j}} \frac{x-\tau }{\beta _j^2}, \\{} & {} \quad \dfrac{\partial r(x)}{\partial \alpha _{j}} = \frac{\lambda _j y^{\alpha _j-1} e^{-\lambda _j y^{\alpha _j}} + \alpha _j \lambda _j y^{\alpha _j-1} \ln y e^{-\lambda _j y^{\alpha _j}} \left( 1-e^{-\lambda _j y^{\alpha _j}}-\lambda _j y^{\alpha _j}\right) }{\left( 1-e^{ -\lambda _j y^{\alpha _j}}\right) ^{2}}, \\{} & {} \quad \dfrac{\partial r(x)}{\partial \lambda _{j}} = \frac{\alpha _j y^{\alpha _j-1} e^{-\lambda _j y^{\alpha _j}}\left( 1-e^{-\lambda _j y^{\alpha _j}}-\lambda _j y^{\alpha _j}\right) }{\left( 1-e^{-\lambda _j y^{\alpha _j}}\right) ^{2}}, \\{} & {} \qquad \dfrac{\partial r(x)}{\partial \beta _{j}} = \frac{\alpha _j \lambda _j z^{\alpha _j-1} e^{-\lambda _j z^{\alpha _j}} \left\{ \alpha _j \lambda _j(z-\tau ) z^{\alpha _j-1}-[1+(\alpha _j-1)(z-\tau )]\left( 1-e^{-\lambda _j z^{\alpha _j}}\right) \right\} }{\beta _j^{2} \left( 1-e^{-\lambda _j z^{\alpha _j}}\right) ^{2}}, \end{aligned}$$

where \(z=\tau +\frac{x-\tau }{\beta _{j}}\) and \(y=\left\{ \begin{array}{ll} x, &{} x < \tau \\ z, &{} x > \tau \end{array}\right.\).

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Tian, Y., Gui, W. Inference and expected total test time for step-stress life test in the presence of complementary risks and incomplete data. Comput Stat 39, 1023–1060 (2024). https://doi.org/10.1007/s00180-023-01343-7

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