IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0307607.html

Exploring mixture estimators in stratified random sampling

Author

Listed:
  • Kanwal Iqbal
  • Syed Muhammad Muslim Raza
  • Tahir Mahmood
  • Muhammad Riaz

Abstract

Advancements in sensor technology have brought a revolution in data generation. Therefore, the study variable and several linearly related auxiliary variables are recorded due to cost-effectiveness and ease of recording. These auxiliary variables are commonly observed as quantitative and qualitative (attributes) variables and are jointly used to estimate the study variable’s population mean using a mixture estimator. For this purpose, this work proposes a family of generalized mixture estimators under stratified sampling to increase efficiency under symmetrical and asymmetrical distributions and study the estimator’s behavior for different sample sizes for its convergence to the Normal distribution. It is found that the proposed estimator estimates the population mean of the study variable with more precision than the competitor estimators under Normal, Uniform, Weibull, and Gamma distributions. It is also revealed that the proposed estimator follows the Cauchy distribution when the sample size is less than 35; otherwise, it converges to normality. Furthermore, the implementation of two real-life datasets related to the health and finance sectors is also presented to support the proposed estimator’s significance.

Suggested Citation

  • Kanwal Iqbal & Syed Muhammad Muslim Raza & Tahir Mahmood & Muhammad Riaz, 2024. "Exploring mixture estimators in stratified random sampling," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-21, September.
    • Handle: RePEc:plo:pone00:0307607
    DOI: 10.1371/journal.pone.0307607
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0307607
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0307607&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0307607?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. Sohaib Ahmad & Sardar Hussain & Muhammad Aamir & Uzma Yasmeen & Javid Shabbir & Zubair Ahmad & Firdous A. Shah, 2021. "Dual Use of Auxiliary Information for Estimating the Finite Population Mean under the Stratified Random Sampling Scheme," Journal of Mathematics, Hindawi, vol. 2021, pages 1-12, November.
    2. Maria Javed & Muhammad Irfan & Sajjad Haider Bhatti & Ronald Onyango & Niansheng Tang, 2021. "A Simulation-Based Study for Progressive Estimation of Population Mean through Traditional and Nontraditional Measures in Stratified Random Sampling," Journal of Mathematics, Hindawi, vol. 2021, pages 1-16, December.
    3. Herman J. Bierens & Donna K. Ginther, 2001. "Integrated Conditional Moment testing of quantile regression models," Empirical Economics, Springer, vol. 26(1), pages 307-324.
    4. Maria M & Ibrahim M Almanjahie & Muhammad Ismail & Ammara Nawaz Cheema, 2023. "Partial stratified ranked set sampling scheme for estimation of population mean and median," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-10, February.
    5. Tolga Zaman, 2021. "An efficient exponential estimator of the mean under stratified random sampling," Mathematical Population Studies, Taylor & Francis Journals, vol. 28(2), pages 104-121, April.
    6. Shashi Bhushan & Anoop Kumar & Sana Shahab & Showkat Ahmad Lone & Md Tanwir Akhtar & Jia-Bao Liu, 2022. "On Efficient Estimation of the Population Mean under Stratified Ranked Set Sampling," Journal of Mathematics, Hindawi, vol. 2022, pages 1-20, October.
    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. Jude, Gareth & Fahimnia, Behnam & Beck, Matthew John, 2026. "Consumer motivation to collaborate in last mile logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 206(C).
    2. repec:bcp:journl:v:9:y:2025:i:10:p:90-120 is not listed on IDEAS
    3. Vo, Hai Long, 2026. "What drives electric vehicle adoption? Perspectives from a geospatial and longitudinal study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 203(C).

    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. Sohaib Ahmad & Sardar Hussain & Uzma Yasmeen & Muhammad Aamir & Javid Shabbir & M El-Morshedy & Afrah Al-Bossly & Zubair Ahmad, 2022. "A simulation study: Using dual ancillary variable to estimate population mean under stratified random sampling," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-19, November.
    2. Sohaib Ahmad & Sardar Hussain & Kalim Ullah & Erum Zahid & Muhammad Aamir & Javid Shabbir & Zubair Ahmad & Huda M Alshanbari & Wejdan Alajlan, 2022. "A simulation study: Improved ratio-in-regression type variance estimator based on dual use of auxiliary variable under simple random sampling," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-14, November.
    3. Latruffe, Laure & Desjeux, Yann & Fogarasi, Jozsef & Bakucs, Lajos Zoltan & Ferto, Imre, 2010. "Technical efficiency and environmental pressures of pig farms in Hungary," 120th Seminar, September 2-4, 2010, Chania, Crete 109385, European Association of Agricultural Economists.
    4. Sungwon Lee & Joon H. Ro, 2020. "Nonparametric Tests for Conditional Quantile Independence with Duration Outcomes," Working Papers 2013, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    5. Fertő, Imre & Bakucs, Lajos Zoltán, 2008. "Érvényes-e a Gibrat-törvény a magyar mezőgazdaságban? [Is Gibrat s law valid for Hungarian agriculture?]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(1), pages 25-38.
    6. Peter Horvath & Jia Li & Zhipeng Liao & Andrew J. Patton, 2022. "A consistent specification test for dynamic quantile models," Quantitative Economics, Econometric Society, vol. 13(1), pages 125-151, January.
    7. Komunjer, Ivana, 2005. "Quasi-maximum likelihood estimation for conditional quantiles," Journal of Econometrics, Elsevier, vol. 128(1), pages 137-164, September.
    8. Jhon James Mora, 2003. "Sheepskin effects and screening in Colombia," Colombian Economic Journal, Academia Colombiana de Ciencias Economicas, Colegio Mayor de Nuestra Senora del Rosario, Pontificia Universidad Javeriana, Universidad de Antioquia, Universidad de los Andes, Universidad del Valle, Universidad Externado de Colombia, Universidad Nacional de Colombia, vol. 1(1), pages 95-108, December.
    9. Escanciano, J.C. & Goh, S.C., 2014. "Specification analysis of linear quantile models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 495-507.
    10. Escanciano, Juan Carlos & Velasco, Carlos, 2010. "Specification tests of parametric dynamic conditional quantiles," Journal of Econometrics, Elsevier, vol. 159(1), pages 209-221, November.
    11. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
    12. Horowitz, Joel L. & Spokoiny, Vladimir G., 2000. "An Adaptive, Rate-Optimal Test of Linearity for Median Regression Models," Working Papers 00-04, University of Iowa, Department of Economics.
    13. Komunjer, Ivana & Vuong, Quang, 2010. "Efficient estimation in dynamic conditional quantile models," Journal of Econometrics, Elsevier, vol. 157(2), pages 272-285, August.
    14. Lajos Zoltán Bakucs & Imre Fertő, 2009. "The growth of family farms in Hungary," Agricultural Economics, International Association of Agricultural Economists, vol. 40(s1), pages 789-795, November.
    15. Escanciano, Juan Carlos & Velasco, Carlos, 2010. "Specification tests of parametric dynamic conditional quantiles," Journal of Econometrics, Elsevier, vol. 159(1), pages 209-221, November.
    16. Juan Carlos Escanciano & Chuan Goh, 2010. "Specification Analysis of Structural Quantile Regression Models," Working Papers tecipa-415, University of Toronto, Department of Economics.
    17. Sun, Yiguo, 2006. "A Consistent Nonparametric Equality Test Of Conditional Quantile Functions," Econometric Theory, Cambridge University Press, vol. 22(4), pages 614-632, August.
    18. Lawrence Dacuycuy, 2006. "Explaining male wage inequality in the Philippines: non-parametric and semiparametric approaches," Applied Economics, Taylor & Francis Journals, vol. 38(21), pages 2497-2511.
    19. repec:hal:journl:peer-00732534 is not listed on IDEAS
    20. Christoph Rothe & Dominik Wied, 2013. "Misspecification Testing in a Class of Conditional Distributional Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 314-324, March.
    21. Priya & Anoop Kumar, 2025. "Robust neutrosophic exponential estimators of population mean in the presence of uncertainty," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(4), pages 3827-3850, August.

    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:plo:pone00:0307607. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.