IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v22y2013i4p501-516.html
   My bibliography  Save this article

A two-sample test when data are contaminated

Author

Listed:
  • Denys Pommeret

Abstract

In this paper we consider the problem of testing whether two samples of contaminated data arise from the same distribution. Is is assumed that the contaminations are additive noises with known, or estimated moments. This situation can also be viewed as two signals observed before and after perturbations. The problem is then to test the equality of both perturbations. The test statistic is based on the polynomials moments of the difference between observations and noises. The test is very simple and allows one to compare two independent as well as two paired contaminated samples. A data driven selection is proposed to choose automatically the number of involved polynomials. We present a simulation study in order to investigate the power of the proposed test within discrete and continuous cases. Real-data examples are presented to illustrate the method. Copyright Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Denys Pommeret, 2013. "A two-sample test when data are contaminated," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(4), pages 501-516, November.
  • Handle: RePEc:spr:stmapp:v:22:y:2013:i:4:p:501-516
    DOI: 10.1007/s10260-013-0235-6
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10260-013-0235-6
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10260-013-0235-6?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. Ledoit, Olivier & Wolf, Michael, 2004. "A well-conditioned estimator for large-dimensional covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
    2. Wang, Xiao-Feng & Wang, Bin, 2011. "Deconvolution Estimation in Measurement Error Models: The R Package decon," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i10).
    3. Inna Chervoneva & Boris Iglewicz, 2005. "Orthogonal bases approach for comparing nonnormal continuous distributions," Biometrika, Biometrika Trust, vol. 92(3), pages 679-690, September.
    4. Kundu, Debasis & Gupta, Rameshwar D., 2009. "Bivariate generalized exponential distribution," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 581-593, April.
    5. Jaromír Antoch & Marie Hušková & Alicja Janic & Teresa Ledwina, 2008. "Data driven rank test for the change point problem," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 68(1), pages 1-15, June.
    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. Hannart, Alexis & Naveau, Philippe, 2014. "Estimating high dimensional covariance matrices: A new look at the Gaussian conjugate framework," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 149-162.
    2. Candelon, B. & Hurlin, C. & Tokpavi, S., 2012. "Sampling error and double shrinkage estimation of minimum variance portfolios," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 511-527.
    3. Avagyan, Vahe & Alonso Fernández, Andrés Modesto & Nogales, Francisco J., 2015. "D-trace Precision Matrix Estimation Using Adaptive Lasso Penalties," DES - Working Papers. Statistics and Econometrics. WS 21775, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Mishra, Anil V., 2016. "Foreign bias in Australian-domiciled mutual fund holdings," Pacific-Basin Finance Journal, Elsevier, vol. 39(C), pages 101-123.
    5. McDowell, Shaun, 2018. "An empirical evaluation of estimation error reduction strategies applied to international diversification," Journal of Multinational Financial Management, Elsevier, vol. 44(C), pages 1-13.
    6. Olivier Ledoit & Michael Wolf, 2003. "Honey, I shrunk the sample covariance matrix," Economics Working Papers 691, Department of Economics and Business, Universitat Pompeu Fabra.
    7. Ali Al-Sharadqah & Majid Mojirsheibani & William Pouliot, 2020. "On the performance of weighted bootstrapped kernel deconvolution density estimators," Statistical Papers, Springer, vol. 61(4), pages 1773-1798, August.
    8. Konno, Yoshihiko, 2009. "Shrinkage estimators for large covariance matrices in multivariate real and complex normal distributions under an invariant quadratic loss," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2237-2253, November.
    9. Yuan, Ke-Hai & Chan, Wai, 2008. "Structural equation modeling with near singular covariance matrices," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4842-4858, June.
    10. Jian Zhang & Li Su, 2015. "Temporal Autocorrelation-Based Beamforming With MEG Neuroimaging Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1375-1388, December.
    11. MohammadAmin Fazli & Parsa Alian & Ali Owfi & Erfan Loghmani, 2021. "RPS: Portfolio Asset Selection using Graph based Representation Learning," Papers 2111.15634, arXiv.org.
    12. Hafner, Christian M. & Linton, Oliver B. & Tang, Haihan, 2020. "Estimation of a multiplicative correlation structure in the large dimensional case," Journal of Econometrics, Elsevier, vol. 217(2), pages 431-470.
    13. Pesaran, M. Hashem & Yamagata, Takashi, 2012. "Testing CAPM with a Large Number of Assets," IZA Discussion Papers 6469, Institute of Labor Economics (IZA).
    14. Füss, Roland & Miebs, Felix & Trübenbach, Fabian, 2014. "A jackknife-type estimator for portfolio revision," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 14-28.
    15. Christian Bongiorno, 2020. "Bootstraps Regularize Singular Correlation Matrices," Working Papers hal-02536278, HAL.
    16. Lassance, Nathan & Vrins, Frédéric, 2021. "Portfolio selection with parsimonious higher comoments estimation," Journal of Banking & Finance, Elsevier, vol. 126(C).
    17. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
    18. Wessel N. van Wieringen & Carel F. W. Peeters & Renee X. de Menezes & Mark A. van de Wiel, 2018. "Testing for pathway (in)activation by using Gaussian graphical models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1419-1436, November.
    19. Arbia, Giuseppe & Bramante, Riccardo & Facchinetti, Silvia & Zappa, Diego, 2018. "Modeling inter-country spatial financial interactions with Graphical Lasso: An application to sovereign co-risk evaluation," Regional Science and Urban Economics, Elsevier, vol. 70(C), pages 72-79.
    20. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Optimal Portfolio Using Factor Graphical Lasso," Working Papers 202025, University of California at Riverside, Department of Economics.

    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:stmapp:v:22:y:2013:i:4:p:501-516. 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.