IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v33y2018i2d10.1007_s00180-017-0787-2.html
   My bibliography  Save this article

Notes on kernel density based mode estimation using more efficient sampling designs

Author

Listed:
  • Hani Samawi

    (Georgia Southern University)

  • Haresh Rochani

    (Georgia Southern University)

  • JingJing Yin

    (Georgia Southern University)

  • Daniel Linder

    (Augusta University)

  • Robert Vogel

    (Georgia Southern University)

Abstract

The mode is a measure of the central tendency as well as the most probable value. Additionally, the mode is not influenced by the tail of the distribution. In the literature the properties and the application of mode estimation is only considered under simple random sampling (SRS). However, ranked set sampling (RSS) is a structural sampling method which improves the efficiency of parameter estimation in many circumstances and typically leads to a reduction in sample size. In this paper we investigate some of the asymptotic properties of kernel density based mode estimation using RSS. We demonstrate that kernel density based mode estimation using RSS is consistent and asymptotically normal with smaller variance than that under SRS. Improved performance of the mode estimation using RSS compared to SRS is supported through a simulation study. An illustration of the computational aspect using a Duchenne muscular dystrophy data set is provided.

Suggested Citation

  • Hani Samawi & Haresh Rochani & JingJing Yin & Daniel Linder & Robert Vogel, 2018. "Notes on kernel density based mode estimation using more efficient sampling designs," Computational Statistics, Springer, vol. 33(2), pages 1071-1090, June.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:2:d:10.1007_s00180-017-0787-2
    DOI: 10.1007/s00180-017-0787-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-017-0787-2
    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/s00180-017-0787-2?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. DiNardo, John & Fortin, Nicole M & Lemieux, Thomas, 1996. "Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach," Econometrica, Econometric Society, vol. 64(5), pages 1001-1044, September.
    2. Johan Lim & Min Chen & Sangun Park & Xinlei Wang & Lynne Stokes, 2014. "Kernel Density Estimator From Ranked Set Samples," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(10-12), pages 2156-2168, May.
    3. Haiying Chen & Elizabeth A. Stasny & Douglas A. Wolfe, 2006. "Unbalanced Ranked Set Sampling for Estimating a Population Proportion," Biometrics, The International Biometric Society, vol. 62(1), pages 150-158, March.
    4. Emili Tortosa-Ausina, 2002. "Financial costs, operating costs, and specialization of Spanish banking firms as distribution dynamics," Applied Economics, Taylor & Francis Journals, vol. 34(17), pages 2165-2176.
    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. Thomas Y. Mathä & Alessandro Porpiglia & Michael Ziegelmeyer, 2014. "Wealth differences across borders and the effect of real estate price dynamics: Evidence from two household surveys," BCL working papers 90, Central Bank of Luxembourg.
    2. Diane Coffey & Ashwini Deshpande & Jeffrey Hammer & Dean Spears, 2019. "Local Social Inequality, Economic Inequality, and Disparities in Child Height in India," Demography, Springer;Population Association of America (PAA), vol. 56(4), pages 1427-1452, August.
    3. Joanna Tyrowicz & Lucas van der Velde, 2017. "When the opportunity knocks: large structural shocks and gender wage gaps," GRAPE Working Papers 2, GRAPE Group for Research in Applied Economics.
    4. Matias Busso & Patrick Kline, 2008. "Do Local Economic Development Programs Work? Evidence from the Federal Empowerment Zone Program," Cowles Foundation Discussion Papers 1639, Cowles Foundation for Research in Economics, Yale University.
    5. Zsófia L. Bárány, 2016. "The Minimum Wage and Inequality: The Effects of Education and Technology," Journal of Labor Economics, University of Chicago Press, vol. 34(1), pages 237-274.
    6. Caliendo, Marco & Wittbrodt, Linda, 2022. "Did the minimum wage reduce the gender wage gap in Germany?," Labour Economics, Elsevier, vol. 78(C).
    7. Corak, Miles & Lauzon, Darren, 2009. "Differences in the distribution of high school achievement: The role of class-size and time-in-term," Economics of Education Review, Elsevier, vol. 28(2), pages 189-198, April.
    8. Jose Pastor & Lorenzo Serrano, 2006. "The Effect of Specialisation on Banks' Efficiency: An International Comparison," International Review of Applied Economics, Taylor & Francis Journals, vol. 20(1), pages 125-149.
    9. Richard V. Burkhauser & Shuaizhang Feng & Stephen P. Jenkins, 2009. "Using The P90/P10 Index To Measure U.S. Inequality Trends With Current Population Survey Data: A View From Inside The Census Bureau Vaults," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 55(1), pages 166-185, March.
    10. Xiaohong Chen & Andres Santos, 2018. "Overidentification in Regular Models," Econometrica, Econometric Society, vol. 86(5), pages 1771-1817, September.
    11. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    12. Bhorat, Haroon & Goga, Sumayya & Stanwix, Benjamin, 2014. "Skills-biased labour demand and the pursuit of inclusive growth in South Africa," WIDER Working Paper Series 130, World Institute for Development Economic Research (UNU-WIDER).
    13. Battisti, Michele & Gatto, Massimo Del & Parmeter, Christopher F., 2022. "Skill-biased technical change and labor market inefficiency," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    14. Burks, Stephen V & Guy, Frederick & Maxwell, Benjamin, 2004. "7. Shifting Gears In The Corner Office: Deregulation And The Earnings Of Trucking Executives," Research in Transportation Economics, Elsevier, vol. 10(1), pages 137-164, January.
    15. Effrosyni Adamopoulou & Ernesto Villanueva, 2020. "Wage determination and the bite of collective contracts in Italy and Spain: evidence from the metal working industry," Working Papers 2036, Banco de España.
    16. Leone Leonida & Marianna Marra & Sergio Scicchitano & Antonio Giangreco & Marco Biagetti, 2020. "Estimating the Wage Premium to Supervision for Middle Managers in Different Contexts: Evidence from Germany and the UK," Work, Employment & Society, British Sociological Association, vol. 34(6), pages 1004-1026, December.
    17. Michael Geruso & Dean Spears, 2018. "Neighborhood Sanitation and Infant Mortality," American Economic Journal: Applied Economics, American Economic Association, vol. 10(2), pages 125-162, April.
    18. Töpfer, Marina, 2017. "Detailed RIF decomposition with selection: The gender pay gap in Italy," Hohenheim Discussion Papers in Business, Economics and Social Sciences 26-2017, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    19. Sergio Longobardi & Margherita Maria Pagliuca & Andrea Regoli, 2018. "Can problem-solving attitudes explain the gender gap in financial literacy? Evidence from Italian students’ data," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(4), pages 1677-1705, July.
    20. Cristiano Perugini & Gaetano Martino, 2008. "Income Inequality Within European Regions: Determinants And Effects On Growth," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 54(3), pages 373-406, September.

    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:33:y:2018:i:2:d:10.1007_s00180-017-0787-2. 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.