IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v22y2013i2p209-226.html
   My bibliography  Save this article

An empirical likelihood ratio based goodness-of-fit test for skew normality

Author

Listed:
  • Wei Ning
  • Grace Ngunkeng

Abstract

In this paper, an empirical likelihood ratio based goodness-of-fit test for the skew normality is proposed. The asymptotic results of the test statistic under the null hypothesis and the alternative hypothesis are derived. Simulations indicate that the Type I error of the proposed test can be well controlled for a given nominal level. The power comparison with other available tests shows that the proposed test is competitive. The test is applied to IQ scores data set and Australian Institute of Sport data set to illustrate the testing procedure. Copyright Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Wei Ning & Grace Ngunkeng, 2013. "An empirical likelihood ratio based goodness-of-fit test for skew normality," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(2), pages 209-226, June.
  • Handle: RePEc:spr:stmapp:v:22:y:2013:i:2:p:209-226
    DOI: 10.1007/s10260-012-0218-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10260-012-0218-z
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10260-012-0218-z?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. Barry Arnold & Robert Beaver & Richard Groeneveld & William Meeker, 1993. "The nontruncated marginal of a truncated bivariate normal distribution," Psychometrika, Springer;The Psychometric Society, vol. 58(3), pages 471-488, September.
    2. Vexler, Albert & Gurevich, Gregory, 2010. "Empirical likelihood ratios applied to goodness-of-fit tests based on sample entropy," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 531-545, February.
    3. C. C. Figueiredo & H. Bolfarine & M. C. Sandoval & C. R. O. P. Lima, 2010. "On the skew-normal calibration model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(3), pages 435-451.
    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. Wenhao Gui & Lei Guo, 2018. "Statistical Inference for the Location and Scale Parameters of the Skew Normal Distribution," Indian Journal of Pure and Applied Mathematics, Springer, vol. 49(4), pages 633-650, December.
    2. Aldo Goia & Ernesto Salinelli & Pascal Sarda, 2015. "A new powerful version of the BUS test of normality," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(3), pages 449-474, September.

    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. Metzler A., 2020. "State dependent correlations in the Vasicek default model," Dependence Modeling, De Gruyter, vol. 8(1), pages 298-329, January.
    2. Denisa Banulescu-Radu & Christophe Hurlin & Jérémy Leymarie & Olivier Scaillet, 2021. "Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures," Management Science, INFORMS, vol. 67(9), pages 5730-5754, September.
    3. Asok K. Nanda & Shovan Chowdhury, 2021. "Shannon's Entropy and Its Generalisations Towards Statistical Inference in Last Seven Decades," International Statistical Review, International Statistical Institute, vol. 89(1), pages 167-185, April.
    4. George J Borjas & Ilpo Kauppinen & Panu Poutvaara, 2019. "Self-selection of Emigrants: Theory and Evidence on Stochastic Dominance in Observable and Unobservable Characteristics," The Economic Journal, Royal Economic Society, vol. 129(617), pages 143-171.
    5. Arthur Pewsey & Héctor Gómez & Heleno Bolfarine, 2012. "Likelihood-based inference for power distributions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(4), pages 775-789, December.
    6. Arismendi, J.C., 2013. "Multivariate truncated moments," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 41-75.
    7. Bhat, Chandra R., 2018. "New matrix-based methods for the analytic evaluation of the multivariate cumulative normal distribution function," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 238-256.
    8. Kampkötter, Patrick & Sliwka, Dirk, 2014. "Wage premia for newly hired employees," Labour Economics, Elsevier, vol. 31(C), pages 45-60.
    9. Metzler A., 2020. "State dependent correlations in the Vasicek default model," Dependence Modeling, De Gruyter, vol. 8(1), pages 298-329, January.
    10. Cruz Lopez, Jorge A. & Harris, Jeffrey H. & Hurlin, Christophe & Pérignon, Christophe, 2017. "CoMargin," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(5), pages 2183-2215, October.
      • Jorge A. Cruz Lopez & Jeffrey H. Harris & Christophe Hurlin & Christophe Pérignon, 2015. "CoMargin," Working Papers halshs-00979440, HAL.
      • Jorge Cruz Lopez & Jeffrey Harris & Christophe Hurlin & Christophe Pérignon, 2017. "CoMargin," Post-Print hal-03579309, HAL.
    11. Pavia, Jose M., 2015. "Testing Goodness-of-Fit with the Kernel Density Estimator: GoFKernel," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(c01).
    12. Ogundimu, Emmanuel O. & Hutton, Jane L., 2015. "On the extended two-parameter generalized skew-normal distribution," Statistics & Probability Letters, Elsevier, vol. 100(C), pages 142-148.
    13. Murray, Paula M. & Browne, Ryan P. & McNicholas, Paul D., 2017. "Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 141-156.
    14. Sharon Lee & Geoffrey McLachlan, 2013. "On mixtures of skew normal and skew $$t$$ -distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(3), pages 241-266, September.
    15. Loperfido, Nicola, 2002. "Statistical implications of selectively reported inferential results," Statistics & Probability Letters, Elsevier, vol. 56(1), pages 13-22, January.
    16. David Elal-Olivero & Juan F. Olivares-Pacheco & Osvaldo Venegas & Heleno Bolfarine & Héctor W. Gómez, 2020. "On Properties of the Bimodal Skew-Normal Distribution and an Application," Mathematics, MDPI, vol. 8(5), pages 1-16, May.
    17. Basso, Rodrigo M. & Lachos, Víctor H. & Cabral, Celso Rômulo Barbosa & Ghosh, Pulak, 2010. "Robust mixture modeling based on scale mixtures of skew-normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 2926-2941, December.
    18. Hossein Negarestani & Ahad Jamalizadeh & Sobhan Shafiei & Narayanaswamy Balakrishnan, 2019. "Mean mixtures of normal distributions: properties, inference and application," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(4), pages 501-528, May.
    19. Antonio Canale & Euloge Clovis Kenne Pagui & Bruno Scarpa, 2016. "Bayesian modeling of university first-year students' grades after placement test," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(16), pages 3015-3029, December.
    20. Dylan Molenaar & Conor Dolan & Paul Boeck, 2012. "The Heteroscedastic Graded Response Model with a Skewed Latent Trait: Testing Statistical and Substantive Hypotheses Related to Skewed Item Category Functions," Psychometrika, Springer;The Psychometric Society, vol. 77(3), pages 455-478, July.

    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:2:p:209-226. 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.