IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/68057.html
   My bibliography  Save this paper

Assessment of Performance of Correlation Estimates in Discrete Bivariate Distributions using Bootstrap Methodology

Author

Listed:
  • Tsagris, Michail
  • Elmatzoglou, Ioannis
  • C. Frangos, Christos

Abstract

Little attention has been given to the correlation coefficient when data come from discrete or continuous non-normal populations. In this article, we consider the efficiency of two correlation coefficients which are from the same family, Pearson's and Spearman's estimators. Two discrete bivariate distributions were examined: the Poisson and the Negative Binomial. The comparison between these two estimators took place using classical and bootstrap techniques for the construction of confidence intervals. Thus, these techniques are also subject to comparison. Simulation studies were also used for the relative efficiency and bias of the two estimators. Pearson's estimator performed slightly better than Spearman's.

Suggested Citation

  • Tsagris, Michail & Elmatzoglou, Ioannis & C. Frangos, Christos, 2012. "Assessment of Performance of Correlation Estimates in Discrete Bivariate Distributions using Bootstrap Methodology," MPRA Paper 68057, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:68057
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/68057/1/MPRA_paper_68057.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Frangos, Chris C. & Schucany, William R., 1990. "Jackknife estimation of the bootstrap acceleration constant," Computational Statistics & Data Analysis, Elsevier, vol. 9(3), pages 271-281, May.
    2. Karlis, Dimitris & Ntzoufras, Ioannis, 2005. "Bivariate Poisson and Diagonal Inflated Bivariate Poisson Regression Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i10).
    3. Greene, William, 2008. "Functional forms for the negative binomial model for count data," Economics Letters, Elsevier, vol. 99(3), pages 585-590, June.
    4. 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.
    5. Michael Dolker & Silas Halperin & D. Divgi, 1982. "Problems with bootstrapping pearson correlations in very small bivariate samples," Psychometrika, Springer;The Psychometric Society, vol. 47(4), pages 529-530, December.
    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. Fragkos, Konstantinos C. & Tsagris, Michail & Frangos, Christos C., 2014. "Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal’s Fail-Safe Number," MPRA Paper 66451, University Library of Munich, Germany.

    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. Riithi, Alexander Njuguna & Irungu, Patrick & Munei , Kimpei, 2015. "Determinants Of Choice Of Alternative Livelihood Diversification Strategies In Solio Resettlement Scheme, Kenya," Dissertations and Theses 269714, University of Nairobi, Department of Agricultural Economics.
    2. Iezzi, Elisa & Lippi Bruni, Matteo & Ugolini, Cristina, 2014. "The role of GP's compensation schemes in diabetes care: Evidence from panel data," Journal of Health Economics, Elsevier, vol. 34(C), pages 104-120.
    3. Mikołaj Czajkowski & Wiktor Budziński & Marianne Zandersen & Wojciech Zawadzki & Uzma Aslam & Ioannis Angelidis & Katarzyna Zagórska, 2024. "The Recreational Value of the Baltic Sea Coast: A Spatially Explicit Site Choice Model Accounting for Environmental Conditions," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(1), pages 135-166, January.
    4. Brendan P. M. McCabe & Christopher L. Skeels, 2020. "Distributions You Can Count On …But What’s the Point?," Econometrics, MDPI, vol. 8(1), pages 1-36, March.
    5. Luke S. Benz & Michael J. Lopez, 2023. "Estimating the change in soccer’s home advantage during the Covid-19 pandemic using bivariate Poisson regression," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 205-232, March.
    6. Alzoubi, Ebraheem Saleem Salem, 2018. "Audit quality, debt financing, and earnings management: Evidence from Jordan," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 30(C), pages 69-84.
    7. Ermagun, Alireza & Stathopoulos, Amanda, 2018. "To bid or not to bid: An empirical study of the supply determinants of crowd-shipping," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 468-483.
    8. Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
    9. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2012. "A Poisson mixture model of discrete choice," Journal of Econometrics, Elsevier, vol. 166(2), pages 184-203.
    10. Mwololo, H. & Nzuma, J. & Ritho, C., 2018. "Is Agricultural Extension a Determinant of Farm Diversification - Evidence from Kenya," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277357, International Association of Agricultural Economists.
    11. Raul Caruso & Marco Di Domizio, 2013. "International hostility and aggressiveness on the soccer pitch: Evidence from European Championships and World Cups for the period 2000–2012," International Area Studies Review, Center for International Area Studies, Hankuk University of Foreign Studies, vol. 16(3), pages 262-273, September.
    12. Hossein Kavand & Marcel Voia, 2018. "Estimation of Health Care Demand and its Implication on Income Effects of Individuals," Springer Proceedings in Business and Economics, in: William H. Greene & Lynda Khalaf & Paul Makdissi & Robin C. Sickles & Michael Veall & Marcel-Cristia (ed.), Productivity and Inequality, pages 275-304, Springer.
    13. Rolfe, John & Gregg, Daniel, 2012. "Valuing Beach Recreation Across a Regional Area: The Great Barrier Reef in Australia," 2012 Conference (56th), February 7-10, 2012, Fremantle, Australia 124433, Australian Agricultural and Resource Economics Society.
    14. Keitometsi Ncube & Charlie M. Shackleton & Brent M. Swallow & Wijaya Dassanayake, 2016. "Impacts of HIV / AIDS on food consumption and wild food use in rural South Africa," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 8(6), pages 1135-1151, December.
    15. Agyekum, Francis K. & Reddy, Krishna & Wallace, Damien & Wellalage, Nirosha H., 2022. "Does technological inclusion promote financial inclusion among SMEs? Evidence from South-East Asian (SEA) countries," Global Finance Journal, Elsevier, vol. 53(C).
    16. Xueming Luo & Michelle Andrews & Zheng Fang & Chee Wei Phang, 2014. "Mobile Targeting," Management Science, INFORMS, vol. 60(7), pages 1738-1756, July.
    17. William Greene, 2007. "Correlation in Bivariate Poisson Regression Model," Working Papers 07-14, New York University, Leonard N. Stern School of Business, Department of Economics.
    18. Donghwan Ki & Sugie Lee, 2019. "Spatial Distribution and Location Characteristics of Airbnb in Seoul, Korea," Sustainability, MDPI, vol. 11(15), pages 1-16, July.
    19. Alice M. Ellyson & Jevay Grooms & Alberto Ortega, 2022. "Flipping the script: The effects of opioid prescription monitoring on specialty‐specific provider behavior," Health Economics, John Wiley & Sons, Ltd., vol. 31(2), pages 297-341, February.
    20. Majo, M.C., 2010. "A microeconometric analysis of health care utilization in Europe," Other publications TiSEM 1cf5fd2f-8146-4ef8-8eb5-e, Tilburg University, School of Economics and Management.

    More about this item

    Keywords

    Bivariate; Bootstrap; Correlation;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions

    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:pra:mprapa:68057. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.