IDEAS home Printed from https://ideas.repec.org/a/wly/jjmath/v2022y2022i1n4530489.html

On Quantiles Estimation Based on Stratified Sampling Using Multiplicative Bias Correction Approach

Author

Listed:
  • Nicholas Makumi
  • Romanus Odhiambo Otieno
  • George Otieno Orwa
  • Alexis Habineza

Abstract

In the context of stratified sampling, we develop a nonparametric regression technique to estimating finite population quantiles in model‐based frameworks using a multiplicative bias correction strategy. Furthermore, the proposed estimator’s asymptotic behavior is presented, and when certain conditions are met, the estimator is observed to be asymptotically unbiased and asymptotically consistent. Simulation studies were conducted to determine the proposed estimator’s performance for the three quartiles of two fictitious populations under varied distributional assumptions. Based on relative biases, mean‐squared errors, and relative root‐mean‐squared errors, the proposed estimator can be extremely satisfactory, according to these findings.

Suggested Citation

  • Nicholas Makumi & Romanus Odhiambo Otieno & George Otieno Orwa & Alexis Habineza, 2022. "On Quantiles Estimation Based on Stratified Sampling Using Multiplicative Bias Correction Approach," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:jjmath:v:2022:y:2022:i:1:n:4530489
    DOI: 10.1155/2022/4530489
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2022/4530489
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/4530489?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
    ---><---

    References listed on IDEAS

    as
    1. Linton, Oliver & Nielsen, Jens Perch, 1994. "A multiplicative bias reduction method for nonparametric regression," Statistics & Probability Letters, Elsevier, vol. 19(3), pages 181-187, February.
    2. Lio, Y. L. & Padgett, W. J., 1991. "A note on the asymptotically optimal bandwidth for Nadaraya's quantile estimator," Statistics & Probability Letters, Elsevier, vol. 11(3), pages 243-249, March.
    3. M. Jones, 1992. "Estimating densities, quantiles, quantile densities and density quantiles," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 44(4), pages 721-727, 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. Okhrin, Ostap & Ristig, Alexander & Sheen, Jeffrey R. & Trück, Stefan, 2015. "Conditional systemic risk with penalized copula," SFB 649 Discussion Papers 2015-038, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Hirukawa, Masayuki, 2010. "Nonparametric multiplicative bias correction for kernel-type density estimation on the unit interval," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 473-495, February.
    3. Wenzhuan Zhang & Yingcun Xia, 2012. "Twicing local linear kernel regression smoothers," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(2), pages 399-417.
    4. P.G. Sankaran & N.N. Midhu, 2017. "Nonparametric estimation of mean residual quantile function under right censoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1856-1874, July.
    5. Yao, Weixin, 2012. "A bias corrected nonparametric regression estimator," Statistics & Probability Letters, Elsevier, vol. 82(2), pages 274-282.
    6. Jens Perch Nielsen & Carsten Tanggaard & M.C. Jones, 2007. "Local Linear Density Estimation for Filtered Survival Data, with Bias Correction," CREATES Research Papers 2007-13, Department of Economics and Business Economics, Aarhus University.
    7. P. Sankaran & N. Unnikrishnan Nair, 2009. "Nonparametric estimation of hazard quantile function," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(6), pages 757-767.
    8. Bischofberger, Stephan M. & Hiabu, Munir & Mammen, Enno & Nielsen, Jens Perch, 2019. "A comparison of in-sample forecasting methods," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 133-154.
    9. Soni, Pooja & Dewan, Isha & Jain, Kanchan, 2012. "Nonparametric estimation of quantile density function," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 3876-3886.
    10. Wolski, M., 2013. "Exploring Nonlinearities in Financial Systemic Risk," CeNDEF Working Papers 13-14, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    11. Chesneau, Christophe & Dewan, Isha & Doosti, Hassan, 2016. "Nonparametric estimation of a quantile density function by wavelet methods," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 161-174.
    12. Enache, Andreea & Florens, Jean-Pierre & Sbai, Erwann, 2023. "A functional estimation approach to the first-price auction models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1564-1588.
    13. Naito, Kanta & Yoshizaki, Masahiro, 2009. "Bandwidth selection for a data sharpening estimator in nonparametric regression," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1465-1486, August.
    14. Ksenia Shakhgildyan, 2025. "Nonparametric identification and estimation of all-pay auction and contest models," Review of Economic Design, Springer;Society for Economic Design, vol. 29(3), pages 545-583, September.
    15. Grigory Franguridi, 2022. "Bias correction and uniform inference for the quantile density function," Papers 2207.09004, arXiv.org.
    16. Karvanen, Juha, 2006. "Estimation of quantile mixtures via L-moments and trimmed L-moments," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 947-959, November.
    17. Willem Albers, 1995. "A two-stage rank test using density estimation," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 47(4), pages 675-691, December.
    18. Funke, Benedikt & Hirukawa, Masayuki, 2021. "Bias correction for local linear regression estimation using asymmetric kernels via the skewing method," Econometrics and Statistics, Elsevier, vol. 20(C), pages 109-130.
    19. Wolski, Marcin, 2018. "Sovereign risk and corporate cost of borrowing: Evidence from a counterfactual study," EIB Working Papers 2018/05, European Investment Bank (EIB).
    20. Ryan Janicki & Tucker S. McElroy, 2016. "Hermite expansion and estimation of monotonic transformations of Gaussian data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(1), pages 207-234, March.

    More about this item

    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:wly:jjmath:v:2022:y:2022:i:1:n:4530489. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/1469 .

    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.