IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v97y2006i1p274-294.html
   My bibliography  Save this article

Local efficiency of a Cramer-von Mises test of independence

Author

Listed:
  • Genest, Christian
  • Quessy, Jean-François
  • Rémillard, Bruno

Abstract

Deheuvels proposed a rank test of independence based on a Cramer-von Mises functional of the empirical copula process. Using a general result on the asymptotic distribution of this process under sequences of contiguous alternatives, the local power curve of Deheuvels' test is computed in the bivariate case and compared to that of competing procedures based on linear rank statistics. The Gil-Pelaez inversion formula is used to make additional comparisons in terms of a natural extension of Pitman's measure of asymptotic relative efficiency.

Suggested Citation

  • Genest, Christian & Quessy, Jean-François & Rémillard, Bruno, 2006. "Local efficiency of a Cramer-von Mises test of independence," Journal of Multivariate Analysis, Elsevier, vol. 97(1), pages 274-294, January.
    • Handle: RePEc:eee:jmvana:v:97:y:2006:i:1:p:274-294
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(05)00031-X
    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. Deheuvels, Paul, 1981. "An asymptotic decomposition for multivariate distribution-free tests of independence," Journal of Multivariate Analysis, Elsevier, vol. 11(1), pages 102-113, March.
    2. David Oakes, 2003. "Copula model generated by Dabrowska's association measure," Biometrika, Biometrika Trust, vol. 90(2), pages 478-481, June.
    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. Kojadinovic, Ivan & Yan, Jun, 2010. "Nonparametric rank-based tests of bivariate extreme-value dependence," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2234-2249, October.
    2. Daniel Berg & Jean‐François Quessy, 2009. "Local Power Analyses of Goodness‐of‐fit Tests for Copulas," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 389-412, September.
    3. Talbi, Marwa & de Peretti, Christian & Belkacem, Lotfi, 2020. "Dynamics and causality in distribution between spot and future precious metals: A copula approach," Resources Policy, Elsevier, vol. 66(C).
    4. Claudio G. Borroni, 2019. "Mutual association measures," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(4), pages 571-591, December.
    5. Helmut Herwartz & Simone Maxand, 2020. "Nonparametric tests for independence: a review and comparative simulation study with an application to malnutrition data in India," Statistical Papers, Springer, vol. 61(5), pages 2175-2201, October.
    6. Liu, Xiaochun, 2015. "Modeling time-varying skewness via decomposition for out-of-sample forecast," International Journal of Forecasting, Elsevier, vol. 31(2), pages 296-311.
    7. Berghaus, Betina & Segers, Johan, 2018. "Weak convergence of the weighted empirical beta copula process," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 266-281.
    8. C Genest & J G Nešlehová & B Rémillard & O A Murphy, 2019. "Testing for independence in arbitrary distributions," Biometrika, Biometrika Trust, vol. 106(1), pages 47-68.
    9. Wanat, Stanisław & Papież, Monika & Śmiech, Sławomir, 2014. "Causality in distribution between European stock markets and commodity prices: Using independence test based on the empirical copula," MPRA Paper 57706, University Library of Munich, Germany.
    10. Genest, Christian & Nešlehová, Johanna G. & Rémillard, Bruno, 2017. "Asymptotic behavior of the empirical multilinear copula process under broad conditions," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 82-110.
    11. Bianchi, Pascal & Elgui, Kevin & Portier, François, 2023. "Conditional independence testing via weighted partial copulas," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
    12. Mangold, Benedikt, 2017. "A multivariate rank test of independence based on a multiparametric polynomial copula," FAU Discussion Papers in Economics 10/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, revised 2017.
    13. Helmut Herwartz & Alexander Lange & Simone Maxand, 2022. "Data‐driven identification in SVARs—When and how can statistical characteristics be used to unravel causal relationships?," Economic Inquiry, Western Economic Association International, vol. 60(2), pages 668-693, April.
    14. Bagkavos, D. & Patil, P.N., 2017. "A new test of independence for bivariate observations," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 117-133.

    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. Pycke, Jean-Renaud, 2003. "Multivariate extensions of the Anderson-Darling process," Statistics & Probability Letters, Elsevier, vol. 63(4), pages 387-399, July.
    2. Gautier Marti & Frank Nielsen & Philippe Donnat & S'ebastien Andler, 2016. "On clustering financial time series: a need for distances between dependent random variables," Papers 1603.07822, arXiv.org.
    3. Koning, Alex J. & Protasov, Vladimir, 2003. "Tail behaviour of Gaussian processes with applications to the Brownian pillow," Journal of Multivariate Analysis, Elsevier, vol. 87(2), pages 370-397, November.
    4. Helmut Herwartz & Simone Maxand, 2020. "Nonparametric tests for independence: a review and comparative simulation study with an application to malnutrition data in India," Statistical Papers, Springer, vol. 61(5), pages 2175-2201, October.
    5. Rémillard, Bruno & Scaillet, Olivier, 2009. "Testing for equality between two copulas," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 377-386, March.
    6. Garratt, Anthony & Henckel, Timo & Vahey, Shaun P., 2023. "Empirically-transformed linear opinion pools," International Journal of Forecasting, Elsevier, vol. 39(2), pages 736-753.
    7. Florencia Leonardi & Matías Lopez‐Rosenfeld & Daniela Rodriguez & Magno T. F. Severino & Mariela Sued, 2021. "Independent block identification in multivariate time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(1), pages 19-33, January.
    8. Einmahl, J.H.J. & McKeague, I.W., 2002. "Empirical Likelihood based on Hypothesis Testing," Other publications TiSEM 402576fa-8c0e-45e2-a394-8, Tilburg University, School of Economics and Management.
    9. Koning, A.J. & Protassov, V., 2001. "Tail behaviour of Gaussian processes with applications to the Brownian pillow," Econometric Institute Research Papers EI 2001-49, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    10. Bianchi, Pascal & Elgui, Kevin & Portier, François, 2023. "Conditional independence testing via weighted partial copulas," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
    11. Christian Genest & Bruno Rémillard, 2004. "Test of independence and randomness based on the empirical copula process," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 13(2), pages 335-369, December.
    12. Berghaus, Betina & Segers, Johan, 2018. "Weak convergence of the weighted empirical beta copula process," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 266-281.
    13. Genest, Christian & Nešlehová, Johanna G. & Rémillard, Bruno, 2013. "On the estimation of Spearman’s rho and related tests of independence for possibly discontinuous multivariate data," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 214-228.
    14. García, Jesús E. & González-López, V.A. & Nelsen, R.B., 2013. "A new index to measure positive dependence in trivariate distributions," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 481-495.
    15. Bakirov, Nail K. & Rizzo, Maria L. & Szekely, Gábor J., 2006. "A multivariate nonparametric test of independence," Journal of Multivariate Analysis, Elsevier, vol. 97(8), pages 1742-1756, September.
    16. Mercadier, Cécile & Roustant, Olivier & Genest, Christian, 2022. "Linking the Hoeffding–Sobol and Möbius formulas through a decomposition of Kuo, Sloan, Wasilkowski, and Woźniakowski," Statistics & Probability Letters, Elsevier, vol. 185(C).
    17. Fernández-Durán Juan José & Gregorio-Domínguez María Mercedes, 2023. "Test of bivariate independence based on angular probability integral transform with emphasis on circular-circular and circular-linear data," Dependence Modeling, De Gruyter, vol. 11(1), pages 1-17, January.
    18. Kojadinovic, Ivan & Holmes, Mark, 2009. "Tests of independence among continuous random vectors based on Cramr-von Mises functionals of the empirical copula process," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1137-1154, July.
    19. Gunky Kim & Mervyn J. Silvapulle & Paramsothy Silvapulle, 2007. "Estimating the Error Distribution in the Multivariate Heteroscedastic Time Series Models," Monash Econometrics and Business Statistics Working Papers 8/07, Monash University, Department of Econometrics and Business Statistics.
    20. Gautier Marti & Philippe Very & Philippe Donnat, 2015. "Toward a generic representation of random variables for machine learning," Working Papers hal-01196883, HAL.

    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:jmvana:v:97:y:2006:i:1:p:274-294. 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/wps/find/journaldescription.cws_home/622892/description#description .

    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.