IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i2d10.1007_s00180-024-01503-3.html
   My bibliography  Save this article

Efficient regression analyses with zero-augmented models based on ranking

Author

Listed:
  • Deborah Kanda

    (University of New Mexico)

  • Jingjing Yin

    (Georgia Southern University)

  • Xinyan Zhang

    (Kennesaw State University)

  • Hani Samawi

    (Georgia Southern University)

Abstract

Several zero-augmented models exist for estimation involving outcomes with large numbers of zero. Two of such models for handling count endpoints are zero-inflated and hurdle regression models. In this article, we apply the extreme ranked set sampling (ERSS) scheme in estimation using zero-inflated and hurdle regression models. We provide theoretical derivations showing superiority of ERSS compared to simple random sampling (SRS) using these zero-augmented models. A simulation study is also conducted to compare the efficiency of ERSS to SRS and lastly, we illustrate applications with real data sets.

Suggested Citation

  • Deborah Kanda & Jingjing Yin & Xinyan Zhang & Hani Samawi, 2025. "Efficient regression analyses with zero-augmented models based on ranking," Computational Statistics, Springer, vol. 40(2), pages 601-632, February.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:2:d:10.1007_s00180-024-01503-3
    DOI: 10.1007/s00180-024-01503-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-024-01503-3
    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/s00180-024-01503-3?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. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273, June.
    2. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    3. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    4. Daniel F. Linder & Jingjing Yin & Haresh Rochani & Hani Samawi & Sanjay Sethi, 2018. "Increased Fisher’s information for parameters of association in count regression via extreme ranks," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(5), pages 1181-1203, March.
    5. Hani M. Samawi & Haresh Rochani & Daniel Linder & Arpita Chatterjee, 2017. "More efficient logistic analysis using moving extreme ranked set sampling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 753-766, March.
    6. Zamanzade, Ehsan & Wang, Xinlei, 2017. "Estimation of population proportion for judgment post-stratification," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 257-269.
    7. 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.
    8. Winkelmann, Rainer & Zimmermann, Klaus F, 1995. "Recent Developments in Count Data Modelling: Theory and Application," Journal of Economic Surveys, Wiley Blackwell, vol. 9(1), pages 1-24, March.
    9. Hani M. Samawi, 2002. "On double extreme rank set sample with application to regression estimator," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1-2), pages 50-63.
    10. Cynthia Chew & Gunther Eysenbach, 2010. "Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-13, November.
    11. Tomeny, Theodore S. & Vargo, Christopher J. & El-Toukhy, Sherine, 2017. "Geographic and demographic correlates of autism-related anti-vaccine beliefs on Twitter, 2009-15," Social Science & Medicine, Elsevier, vol. 191(C), pages 168-175.
    12. David A Broniatowski & Michael J Paul & Mark Dredze, 2013. "National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, 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. Sanmitra Bhattacharya & Padmini Srinivasan & Phil Polgreen, 2014. "Engagement with Health Agencies on Twitter," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-12, November.
    2. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    3. Moritz Berger & Gerhard Tutz, 2021. "Transition models for count data: a flexible alternative to fixed distribution models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1259-1283, October.
    4. Nan-Ting Liu & Feng-Chang Lin & Yu-Shan Shih, 2020. "Count regression trees," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 5-27, March.
    5. John Haslett & Andrew C. Parnell & John Hinde & Rafael de Andrade Moral, 2022. "Modelling Excess Zeros in Count Data: A New Perspective on Modelling Approaches," International Statistical Review, International Statistical Institute, vol. 90(2), pages 216-236, August.
    6. Gozde Ozonder & Eric J. Miller, 2021. "Longitudinal analysis of activity generation in the Greater Toronto and Hamilton Area," Transportation, Springer, vol. 48(3), pages 1149-1183, June.
    7. Olivier Finance & Clémentine Cottineau, 2019. "Are the absent always wrong? Dealing with zero values in urban scaling," Environment and Planning B, , vol. 46(9), pages 1663-1677, November.
    8. Greene, William, 2007. "Functional Form and Heterogeneity in Models for Count Data," Foundations and Trends(R) in Econometrics, now publishers, vol. 1(2), pages 113-218, August.
    9. J. M. C. Santos Silva & Silvana Tenreyro, 2022. "The Log of Gravity at 15," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 21(3), pages 423-437, September.
    10. Evgenii V. Gilenko & Elena A. Mironova, 2017. "Modern claim frequency and claim severity models: An application to the Russian motor own damage insurance market," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1311097-131, January.
    11. José M. R. Murteira & Mário A. G. Augusto, 2017. "Hurdle models of repayment behaviour in personal loan contracts," Empirical Economics, Springer, vol. 53(2), pages 641-667, September.
    12. Andre Jungmittag, 2019. "Service trade restrictiveness and internationalisation of retail trade," International Economics and Economic Policy, Springer, vol. 16(2), pages 293-333, April.
    13. Christian Balcells, 2022. "Determinants of firm boundaries and organizational performance: an empirical investigation of the Chilean truck market," Journal of Evolutionary Economics, Springer, vol. 32(2), pages 423-461, April.
    14. Zeynep Ertem & Dorrie Raymond & Lauren Ancel Meyers, 2018. "Optimal multi-source forecasting of seasonal influenza," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-16, September.
    15. Jose L Herrera & Ravi Srinivasan & John S Brownstein & Alison P Galvani & Lauren Ancel Meyers, 2016. "Disease Surveillance on Complex Social Networks," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-16, July.
    16. Luigi Aldieri & Maxim N. Kotsemir & Concetto Paolo Vinci, 2020. "The Effects of Collaboration on Research Performance of Universities: an Analysis by Federal District and Scientific Fields in Russia," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 11(2), pages 766-787, June.
    17. Ibrahim Musa & Hyun Woo Park & Lkhagvadorj Munkhdalai & Keun Ho Ryu, 2018. "Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization," Sustainability, MDPI, vol. 10(10), pages 1-20, September.
    18. Mihaela Covrig & Iulian Mircea & Gheorghita Zbaganu & Alexandru Coser & Alexandru Tindeche, 2015. "Using R In Generalized Linear Models," Romanian Statistical Review, Romanian Statistical Review, vol. 63(3), pages 33-45, September.
    19. Filipe Sengo Furtado & Thomas Reutterer & Nadine Schröder, 2022. "The carrot and the stick in online reviews: determinants of un-/helpfulness voting choices," Journal of Business Economics, Springer, vol. 92(4), pages 565-590, May.
    20. Maximilian H. M. Hatz & Jonas Schreyögg & Aleksandra Torbica & Giuseppe Boriani & Carl R. B. Blankart, 2017. "Adoption Decisions for Medical Devices in the Field of Cardiology: Results from a European Survey," Health Economics, John Wiley & Sons, Ltd., vol. 26(S1), pages 124-144, February.

    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:40:y:2025:i:2:d:10.1007_s00180-024-01503-3. 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.