IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v65y2024i1d10.1007_s00362-022-01385-x.html
   My bibliography  Save this article

Jackknife empirical likelihood ratio test for testing mean time to failure order

Author

Listed:
  • Deemat C. Mathew

    (St. Thomas College Palai)

  • Reeba Mary Alex

    (St. Thomas College Palai)

  • Sudheesh K. Kattumannil

    (Indian Statistical Institute)

Abstract

In this paper, we propose a new non-parametric test for testing mean time to failure order. The asymptotic properties of the proposed test statistic are studied. We also develop a jackknife empirical likelihood (JEL) ratio test for testing the mean time to failure order. Using the Monte Carlo simulation study, we establish that the JEL ratio test has good power under various alternatives. Finally, we illustrate the proposed test procedure using two real data sets.

Suggested Citation

  • Deemat C. Mathew & Reeba Mary Alex & Sudheesh K. Kattumannil, 2024. "Jackknife empirical likelihood ratio test for testing mean time to failure order," Statistical Papers, Springer, vol. 65(1), pages 79-92, February.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:1:d:10.1007_s00362-022-01385-x
    DOI: 10.1007/s00362-022-01385-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00362-022-01385-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00362-022-01385-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Jing, Bing-Yi & Yuan, Junqing & Zhou, Wang, 2009. "Jackknife Empirical Likelihood," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1224-1232.
    2. Bhattacharyya, Dhrubasish & Khan, Ruhul Ali & Mitra, Murari, 2020. "A test of exponentiality against DMTTF alternatives via L-statistics," Statistics & Probability Letters, Elsevier, vol. 165(C).
    3. Sudheesh K. Kattumannil & P. Anisha, 2019. "A simple non-parametric test for decreasing mean time to failure," Statistical Papers, Springer, vol. 60(1), pages 73-87, February.
    4. K. K. Sudheesh & G. Asha & K. M. Jagathnath Krishna, 2021. "On the mean time to failure of an age-replacement model in discrete time," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(11), pages 2569-2585, June.
    5. Yang, Hanfang & Zhao, Yichuan, 2015. "Smoothed jackknife empirical likelihood inference for ROC curves with missing data," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 123-138.
    6. Zhang, Zhengjia & Liu, Tianqing & Zhang, Baoxue, 2016. "Jackknife empirical likelihood inferences for the population mean with ranked set samples," Statistics & Probability Letters, Elsevier, vol. 108(C), pages 16-22.
    7. Zhao, Yichuan & Meng, Xueping & Yang, Hanfang, 2015. "Jackknife empirical likelihood inference for the mean absolute deviation," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 92-101.
    8. Yongcheng Qi, 2018. "Jackknife Empirical Likelihood Methods," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 7(2), pages 20-22, June.
    9. Ruhul Ali Khan & Dhrubasish Bhattacharyya & Murari Mitra, 2021. "Exact and asymptotic tests of exponentiality against nonmonotonic mean time to failure type alternatives," Statistical Papers, Springer, vol. 62(6), pages 3015-3045, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yongcheng Qi, 2018. "Jackknife Empirical Likelihood Methods," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 7(2), pages 20-22, June.
    2. Amorim, G. & Thas, O. & Vermeulen, K. & Vansteelandt, S. & De Neve, J., 2018. "Small sample inference for probabilistic index models," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 137-148.
    3. Xue Yu & Yichuan Zhao, 2019. "Jackknife empirical likelihood inference for the accelerated failure time model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 269-288, March.
    4. Da Chen & Linlin Dai & Yichuan Zhao, 2024. "Jackknife empirical likelihood for the correlation coefficient with additive distortion measurement errors," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(4), pages 1129-1159, December.
    5. Zhao, Yichuan & Su, Yueju & Yang, Hanfang, 2020. "Jackknife empirical likelihood inference for the Pietra ratio," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    6. Hui-Ling Lin & Zhouping Li & Dongliang Wang & Yichuan Zhao, 2017. "Jackknife empirical likelihood for the error variance in linear models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(2), pages 151-166, April.
    7. Khan, Ruhul Ali, 2023. "Two-sample nonparametric test for proportional reversed hazards," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    8. Hanfang Yang & Yichuan Zhao, 2017. "Smoothed jackknife empirical likelihood for the difference of two quantiles," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(5), pages 1059-1073, October.
    9. Yu, Xue & Zhao, Yichuan, 2019. "Empirical likelihood inference for semi-parametric transformation models with length-biased sampling," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 115-125.
    10. Walid B. H. Etman & Mohamed S. Eliwa & Hana N. Alqifari & Mahmoud El-Morshedy & Laila A. Al-Essa & Rashad M. EL-Sagheer, 2023. "The NBRULC Reliability Class: Mathematical Theory and Goodness-of-Fit Testing with Applications to Asymmetric Censored and Uncensored Data," Mathematics, MDPI, vol. 11(13), pages 1-22, June.
    11. Avhad, Ganesh Vishnu & Lahiri, Ananya, 2025. "A jackknife empirical likelihood ratio test for log-symmetric distributions," Statistics & Probability Letters, Elsevier, vol. 222(C).
    12. Lisa Parveen & Ruhul Ali Khan & Murari Mitra, 2024. "A two sample nonparametric test for variability via empirical likelihood methods," Statistical Papers, Springer, vol. 65(7), pages 4243-4265, September.
    13. Yueheng An & Yichuan Zhao, 2018. "Jackknife empirical likelihood for the difference of two volumes under ROC surfaces," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(4), pages 789-806, August.
    14. Zhao, Yichuan & Meng, Xueping & Yang, Hanfang, 2015. "Jackknife empirical likelihood inference for the mean absolute deviation," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 92-101.
    15. Zhouping Li & Jinfeng Xu & Wang Zhou, 2016. "On Nonsmooth Estimating Functions via Jackknife Empirical Likelihood," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 49-69, March.
    16. Heng Wang & Ping-Shou Zhong, 2017. "Order-restricted inference for means with missing values," Biometrics, The International Biometric Society, vol. 73(3), pages 972-980, September.
    17. Vanderford Courtney & Sang Yongli & Dang Xin, 2020. "Two symmetric and computationally efficient Gini correlations," Dependence Modeling, De Gruyter, vol. 8(1), pages 373-395, January.
    18. Ana M. Bianco & Graciela Boente & Wenceslao González–Manteiga & Ana Pérez–González, 2023. "Estimators for ROC curves with missing biomarkers values and informative covariates," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 931-956, September.
    19. Yukitoshi Matsushita & Taisuke Otsu, 2018. "Likelihood Inference on Semiparametric Models: Average Derivative and Treatment Effect," The Japanese Economic Review, Springer, vol. 69(2), pages 133-155, June.
    20. Liang, Wei & Dai, Hongsheng & He, Shuyuan, 2019. "Mean Empirical Likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 155-169.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stpapr:v:65:y:2024:i:1:d:10.1007_s00362-022-01385-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.