IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v55y2011i4p1509-1520.html
   My bibliography  Save this article

Design of experiments for bivariate binary responses modelled by Copula functions

Author

Listed:
  • Denman, N.G.
  • McGree, J.M.
  • Eccleston, J.A.
  • Duffull, S.B.

Abstract

Optimal design for generalized linear models has primarily focused on univariate data. Often experiments are performed that have multiple dependent responses described by regression type models, and it is of interest and of value to design the experiment for all these responses. This requires a multivariate distribution underlying a pre-chosen model for the data. Here, we consider the design of experiments for bivariate binary data which are dependent. We explore Copula functions which provide a rich and flexible class of structures to derive joint distributions for bivariate binary data. We present methods for deriving optimal experimental designs for dependent bivariate binary data using Copulas, and demonstrate that, by including the dependence between responses in the design process, more efficient parameter estimates are obtained than by the usual practice of simply designing for a single variable only. Further, we investigate the robustness of designs with respect to initial parameter estimates and Copula function, and also show the performance of compound criteria within this bivariate binary setting.

Suggested Citation

  • Denman, N.G. & McGree, J.M. & Eccleston, J.A. & Duffull, S.B., 2011. "Design of experiments for bivariate binary responses modelled by Copula functions," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1509-1520, April.
  • Handle: RePEc:eee:csdana:v:55:y:2011:i:4:p:1509-1520
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00312-9
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Bénédicte Vidaillet & V. d'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
    2. Trivedi, Pravin K. & Zimmer, David M., 2007. "Copula Modeling: An Introduction for Practitioners," Foundations and Trends(R) in Econometrics, now publishers, vol. 1(1), pages 1-111, April.
    3. Alberini Anna, 1995. "Optimal Designs for Discrete Choice Contingent Valuation Surveys: Single-Bound, Double-Bound, and Bivariate Models," Journal of Environmental Economics and Management, Elsevier, vol. 28(3), pages 287-306, May.
    4. Barbara J. Kanninen, 1993. "Optimal Experimental Design for Double-Bounded Dichotomous Choice Contingent Valuation," Land Economics, University of Wisconsin Press, vol. 69(2), pages 138-146.
    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. Laura Deldossi & Silvia Angela Osmetti & Chiara Tommasi, 2019. "Optimal design to discriminate between rival copula models for a bivariate binary response," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 147-165, March.
    2. S. G. J. Senarathne & C. C. Drovandi & J. M. McGree, 2020. "Bayesian sequential design for Copula models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 454-478, June.
    3. Elisa Perrone & Andreas Rappold & Werner G. Müller, 2017. "$$D_s$$ D s -optimality in copula models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(3), pages 403-418, August.

    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. Marc Gronwald & Janina Ketterer & Stefan Trück, 2011. "The Dependence Structure between Carbon Emission Allowances and Financial Markets - A Copula Analysis," CESifo Working Paper Series 3418, CESifo.
    2. Kim, GwanSeon & Petrolia, Daniel R. & Interis, Matthew G., 2012. "A Method for Improving Welfare Estimates from Multiple-Referendum Surveys," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 37(2), pages 1-12, August.
    3. Cheng-Te Lin & Yu-Sheng Huang & Lu-Wen Liao & Chung-Te Ting, 2020. "Measuring Consumer Willingness to Pay to Reduce Health Risks of Contracting Dengue Fever," IJERPH, MDPI, vol. 17(5), pages 1-15, March.
    4. Paulo Horta & Carlos Mendes & Isabel Vieira, 2008. "Contagion effects of the US Subprime Crisis on Developed Countries," CEFAGE-UE Working Papers 2008_08, University of Evora, CEFAGE-UE (Portugal).
    5. Richard T. Carson, 2011. "Contingent Valuation," Books, Edward Elgar Publishing, number 2489.
    6. Luchini, Stéphane & Watson, Verity, 2013. "Uncertainty and framing in a valuation task," Journal of Economic Psychology, Elsevier, vol. 39(C), pages 204-214.
    7. John R. Crooker & Aju J. Fenn, 2008. "Estimating Local Welfare Generated by a Professional Sports Team: An Application to the Minnesota Vikings under Threat of Relocation," Working Papers 0805, University of Central Missouri, Department of Economics & Finance, revised May 2008.
    8. Zapata, Samuel D. & Carpio, Carlos E., . "Distribution-Free Methods to Estimate Willingness to Pay Models Using Discrete Response Valuation Data," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 49(1).
    9. W. George Hutchinson & Riccardo Scarpa & Susan M. Chilton & T. McCallion, 2001. "Parametric and Non‐Parametric Estimates of Willingness to Pay for Forest Recreation in Northern Ireland: A Discrete Choice Contingent Valuation Study with Follow‐Ups," Journal of Agricultural Economics, Wiley Blackwell, vol. 52(1), pages 104-122, January.
    10. Ellinor Fackle-Fornius & Linda Anna W�nstr�m, 2014. "Minimax D-optimal designs of contingent valuation experiments: willingness to pay for environmentally friendly clothes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(4), pages 895-908, April.
    11. Kuralbayeva, Karlygash & Stefanski, Radoslaw, 2013. "Windfalls, structural transformation and specialization," Journal of International Economics, Elsevier, vol. 90(2), pages 273-301.
    12. José L Oviedo & Pablo Campos & Alejandro Caparrós, 2022. "Contingent valuation of landowner demand for forest amenities: application in Andalusia, Spain [Optimal design for discrete choice contingent valuation surveys: single-bound, double-bound and bivar," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 49(3), pages 615-643.
    13. Vinh, Andrea & Griffiths, William E. & Chotikapanich, Duangkamon, 2010. "Bivariate income distributions for assessing inequality and poverty under dependent samples," Economic Modelling, Elsevier, vol. 27(6), pages 1473-1483, November.
    14. Anna Alberini, 2004. "Robustness of VSL Values from Contingent Valuation Surveys," Working Papers 2004.135, Fondazione Eni Enrico Mattei.
    15. Fredrik Carlsson & Peter Martinsson, 2003. "Design techniques for stated preference methods in health economics," Health Economics, John Wiley & Sons, Ltd., vol. 12(4), pages 281-294, April.
    16. Steffens, Karin & Lupi, Frank & Kanninen, Barbara J. & Hoehn, P., 2000. "Implementing an Optimal Experiental Design for a Binary Choice Experiments: An Application to Bird Watching in Michigan," Western Region Archives 321669, Western Region - Western Extension Directors Association (WEDA).
    17. Scarpa, Riccardo & Rose, John M., 2008. "Design efficiency for non-market valuation with choice modelling: how to measure it, what to report and why," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 52(3), pages 1-30.
    18. Ferrini, Silvia & Scarpa, Riccardo, 2007. "Designs with a priori information for nonmarket valuation with choice experiments: A Monte Carlo study," Journal of Environmental Economics and Management, Elsevier, vol. 53(3), pages 342-363, May.
    19. Yen, Steven T. & Yuan, Yan & Liu, Xiaowen, 2009. "Alcohol consumption by men in China: A non-Gaussian censored system approach," China Economic Review, Elsevier, vol. 20(2), pages 162-173, June.
    20. Kathleen Segerson & Catherine L. Kling & Nancy E. Bockstael, 2022. "Contributions of women at the intersection of agricultural economics and environmental and natural resource economics," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(1), pages 38-53, March.

    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:eee:csdana:v:55:y:2011:i:4:p:1509-1520. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.