IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v51y2007i5p2428-2441.html
   My bibliography  Save this article

Goodness of fit tests via exponential series density estimation

Author

Listed:
  • Marsh, Patrick

Abstract

This paper explores the properties of a new nonparametric goodness of fit test, based on the likelihood ratio test of Portnoy (1988). It is applied via the consistent series density estimator of Crain (1974) and Barron and Sheu (1991). The asymptotic properties are established as trivial corollaries to the results of those papers as well as from similar results in Marsh (2000) and Claeskens and Hjort (2004). The paper focuses on the computational and numerical properties. Specifically it is found that the choice of approximating basis is not crucial and that the choice of model dimension, through consistent selection criteria, yields a feasible procedure. Extensive numerical experiments show that the usage of asymptotic critical values is feasible in moderate sample seizes. More importantly the new tests are shown to have significantly more power than established tests such as the Kolmogorov-Smirnov, Cramer-von Mises or Anderson-Darling. Indeed, for certain interesting alternatives the power of the proposed tests may be several times that of the established ones.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Marsh, Patrick, 2007. "Goodness of fit tests via exponential series density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2428-2441, February.
    • Handle: RePEc:eee:csdana:v:51:y:2007:i:5:p:2428-2441
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(06)00283-0
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Gerda Claeskens & Nils Lid Hjort, 2004. "Goodness of Fit via Non‐parametric Likelihood Ratios," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(4), pages 487-513, December.
    2. Patrick Marsh, "undated". "Nonparametric Likelihood Ratio Tests," Discussion Papers 00/56, Department of Economics, University of York.
    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. Patrick Marsh, 2006. "Data Driven Likelihood Ratio Tests for Goodness-of-Fit with Estimated Parameters," Discussion Papers 06/20, Department of Economics, University of York.
    2. Patrick Marsh, 2010. "A two-sample nonparametric likelihood ratio test," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(8), pages 1053-1065.
    3. Patrick Marsh, 2019. "Nonparametric conditional density specification testing and quantile estimation; with application to S&P500 returns," Discussion Papers 19/02, University of Nottingham, Granger Centre for Time Series Econometrics.
    4. Patrick Marsh, 2019. "Nonparametric series density estimation and testing," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(1), pages 77-99, March.

    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. Olivier Thas, 2009. "Comments on: Goodness-of-fit tests in mixed modes," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(2), pages 260-264, August.
    2. Best, D.J. & Rayner, J.C.W. & Thas, O., 2008. "Comparison of some tests of fit for the Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5338-5343, August.
    3. Patrick Marsh, 2019. "Nonparametric conditional density specification testing and quantile estimation; with application to S&P500 returns," Discussion Papers 19/02, University of Nottingham, Granger Centre for Time Series Econometrics.
    4. Patrick Marsh, 2010. "A two-sample nonparametric likelihood ratio test," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(8), pages 1053-1065.
    5. Patrick Marsh, 2006. "Data Driven Likelihood Ratio Tests for Goodness-of-Fit with Estimated Parameters," Discussion Papers 06/20, Department of Economics, University of York.
    6. Gerda Claeskens & Jeffrey Hart, 2009. "Goodness-of-fit tests in mixed models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(2), pages 213-239, August.
    7. Consentino, Fabrizio & Claeskens, Gerda, 2010. "Order selection tests with multiply imputed data," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2284-2295, October.
    8. Fang, Ying & Li, Qi & Wu, Ximing & Zhang, Daiqiang, 2015. "A data-driven smooth test of symmetry," Journal of Econometrics, Elsevier, vol. 188(2), pages 490-501.
    9. Patrick Marsh, 2019. "Nonparametric series density estimation and testing," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(1), pages 77-99, March.
    10. Emiliano, Paulo C. & Vivanco, Mário J.F. & de Menezes, Fortunato S., 2014. "Information criteria: How do they behave in different models?," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 141-153.
    11. J. Beirlant & G. Claeskens & C. Croux & H. Degryse & H. Dewachter & G. Dhaene & J. Dhaene & I. Gijbels & M. Goovaerts & M. Hubert & F. Roodhooft & W. Schouten & M. Willekens, 2005. "Managing Uncertainty: Financial, Actuarial and Statistical Modeling," Review of Business and Economic Literature, KU Leuven, Faculty of Economics and Business (FEB), Review of Business and Economic Literature, vol. 0(1), pages 23-48.
    12. Nicolai Bissantz & Gerda Claeskens & Hajo Holzmann & Axel Munk, 2009. "Testing for lack of fit in inverse regression—with applications to biophotonic imaging," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 25-48, January.
    13. Ali Charkhi & Gerda Claeskens, 2018. "Asymptotic post-selection inference for the Akaike information criterion," Biometrika, Biometrika Trust, vol. 105(3), pages 645-664.
    14. Hart, Jeffrey D. & Choi, Taeryon & Yi, Seongbaek, 2016. "Frequentist nonparametric goodness-of-fit tests via marginal likelihood ratios," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 120-132.

    More about this item

    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:eee:csdana:v:51:y:2007:i:5:p:2428-2441. 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.