IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v37y2022i5d10.1007_s00180-022-01198-4.html
   My bibliography  Save this article

Improved confidence intervals based on ranked set sampling designs within a parametric bootstrap approach

Author

Listed:
  • Cesar Augusto Taconeli

    (Federal University of Paraná)

  • Idemauro Antonio Rodrigues Lara

    (University of São Paulo)

Abstract

We study the problem of obtaining confidence intervals (CIs) within a parametric framework under different ranked set sampling (RSS) designs. This is an important research issue since it has not yet been adequately addressed in the RSS literature. We focused on evaluating CIs based on a recently developed parametric bootstrap approach, and the asymptotic maximum likelihood CIs under simple random sampling (SRS) was taken as the counterpart. A comprehensive simulation study was carried out to evaluate the accuracy and precision of the CIs. We have considered as sampling designs the paired RSS, neoteric RSS, and double RSS, besides the original RSS and SRS. Different estimation methods and bootstrap CIs were evaluated. In addition, the robustness of the CIs to imperfect ranking was evaluated by inducing varied levels of ranking errors. The simulated results allowed us to identify accurate bootstrap CIs based on RSS and some of its extensions, which outperform the usual asymptotic or bootstrap CIs based on SRS in terms of accuracy (coverage rate) and/or precision (average width).

Suggested Citation

  • Cesar Augusto Taconeli & Idemauro Antonio Rodrigues Lara, 2022. "Improved confidence intervals based on ranked set sampling designs within a parametric bootstrap approach," Computational Statistics, Springer, vol. 37(5), pages 2267-2293, November.
  • Handle: RePEc:spr:compst:v:37:y:2022:i:5:d:10.1007_s00180-022-01198-4
    DOI: 10.1007/s00180-022-01198-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-022-01198-4
    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/s00180-022-01198-4?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Modarres, Reza & Hui, Terrence P. & Zheng, Gang, 2006. "Resampling methods for ranked set samples," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1039-1050, November.
    2. Al-Saleh, M. Fraiwan & Al-Kadiri, M. Ali, 2000. "Double-ranked set sampling," Statistics & Probability Letters, Elsevier, vol. 48(2), pages 205-212, June.
    3. Cesar Augusto Taconeli & Wagner Hugo Bonat, 2020. "On the performance of estimation methods under ranked set sampling," Computational Statistics, Springer, vol. 35(4), pages 1805-1826, December.
    4. Anatolyev, Stanislav & Kosenok, Grigory, 2005. "An Alternative To Maximum Likelihood Based On Spacings," Econometric Theory, Cambridge University Press, vol. 21(2), pages 472-476, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zeinab Akbari Ghamsari & Ehsan Zamanzade & Majid Asadi, 2024. "Using nomination sampling in estimating the area under the ROC curve," Computational Statistics, Springer, vol. 39(5), pages 2721-2742, July.

    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. Manal M. Yousef & Amal S. Hassan & Abdullah H. Al-Nefaie & Ehab M. Almetwally & Hisham M. Almongy, 2022. "Bayesian Estimation Using MCMC Method of System Reliability for Inverted Topp–Leone Distribution Based on Ranked Set Sampling," Mathematics, MDPI, vol. 10(17), pages 1-26, August.
    2. Mohammad Al-Saleh & Said Al-Hadhrami, 2003. "Estimation of the mean of the exponential distribution using moving extremes ranked set sampling," Statistical Papers, Springer, vol. 44(3), pages 367-382, July.
    3. Heba F. Nagy & Amer Ibrahim Al-Omari & Amal S. Hassan & Ghadah A. Alomani, 2022. "Improved Estimation of the Inverted Kumaraswamy Distribution Parameters Based on Ranked Set Sampling with an Application to Real Data," Mathematics, MDPI, vol. 10(21), pages 1-19, November.
    4. Suparna Basu & Sanjay Kumar Singh & Umesh Singh, 2017. "Parameter estimation of inverse Lindley distribution for Type-I censored data," Computational Statistics, Springer, vol. 32(1), pages 367-385, March.
    5. Al-Saleh, Mohammad Fraiwan & Samuh, Monjed Hisham, 2008. "On multistage ranked set sampling for distribution and median estimation," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2066-2078, January.
    6. Amer Ibrahim Al-Omari & Mohammad Fraiwan Al-Saleh, 2010. "Improvement in estimating the population mean using two-stage balanced groups ranked set sampling," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(2), pages 185-196.
    7. Ehab M. Almetwally & Hanan A. Haj Ahmad, 2020. "A new generalization of the Pareto distribution and its applications," Statistics in Transition New Series, Polish Statistical Association, vol. 21(5), pages 61-84, December.
    8. B. L. Robertson & O. Ozturk & O. Kravchuk & J. A. Brown, 2022. "Spatially Balanced Sampling with Local Ranking," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 622-639, December.
    9. Liang Wang & Sanku Dey & Yogesh Mani Tripathi, 2022. "Classical and Bayesian Inference of the Inverse Nakagami Distribution Based on Progressive Type-II Censored Samples," Mathematics, MDPI, vol. 10(12), pages 1-18, June.
    10. Santu Ghosh & Arpita Chatterjee & N. Balakrishnan, 2017. "Nonparametric confidence intervals for ranked set samples," Computational Statistics, Springer, vol. 32(4), pages 1689-1725, December.
    11. Grigoriy Volovskiy & Udo Kamps, 2020. "Maximum product of spacings prediction of future record values," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(7), pages 853-868, October.
    12. Suparna Basu & Sanjay K. Singh & Umesh Singh, 2019. "Estimation of Inverse Lindley Distribution Using Product of Spacings Function for Hybrid Censored Data," Methodology and Computing in Applied Probability, Springer, vol. 21(4), pages 1377-1394, December.
    13. Hanem Mohamed & Salwa A. Mousa & Amina E. Abo-Hussien & Magda M. Ismail, 2022. "Estimation of the Daily Recovery Cases in Egypt for COVID-19 Using Power Odd Generalized Exponential Lomax Distribution," Annals of Data Science, Springer, vol. 9(1), pages 71-99, February.
    14. Cesar Augusto Taconeli & Suely Ruiz Giolo, 2020. "Maximum likelihood estimation based on ranked set sampling designs for two extensions of the Lindley distribution with uncensored and right-censored data," Computational Statistics, Springer, vol. 35(4), pages 1827-1851, December.
    15. M. Mahdizadeh & E. Strzalkowska-Kominiak, 2017. "Resampling based inference for a distribution function using censored ranked set samples," Computational Statistics, Springer, vol. 32(4), pages 1285-1308, December.
    16. Mohamed Sief & Xinsheng Liu & Abd El-Raheem Mohamed Abd El-Raheem, 2024. "Inference for a constant-stress model under progressive type-II censored data from the truncated normal distribution," Computational Statistics, Springer, vol. 39(5), pages 2791-2820, July.
    17. Zamanzade, Elham & Parvardeh, Afshin & Asadi, Majid, 2019. "Estimation of mean residual life based on ranked set sampling," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 35-55.
    18. Mazen Nassar & Ahmed Elshahhat, 2023. "Statistical Analysis of Inverse Weibull Constant-Stress Partially Accelerated Life Tests with Adaptive Progressively Type I Censored Data," Mathematics, MDPI, vol. 11(2), pages 1-29, January.
    19. Robertson, B.L. & Reale, M. & Price, C.J. & Brown, J.A., 2021. "Quasi-random ranked set sampling," Statistics & Probability Letters, Elsevier, vol. 171(C).
    20. Drikvandi, Reza & Modarres, Reza & Jalilian, Abdullah H., 2011. "A bootstrap test for symmetry based on ranked set samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1807-1814, April.

    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:compst:v:37:y:2022:i:5:d:10.1007_s00180-022-01198-4. 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.