IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v24y2015i3p449-474.html
   My bibliography  Save this article

A new powerful version of the BUS test of normality

Author

Listed:
  • Aldo Goia
  • Ernesto Salinelli
  • Pascal Sarda

Abstract

In this paper we introduce a modified version of the BUS test, which we call NBUS (New Borovkov–Utev Statistic). This latter defines a family of goodness of fit tests that can be used to detect normality against alternative hypothesis of which all moments up to the fifth exist. The test statistic depends on empirical moments and real parameters that have to be chosen appropriately. The good abilities of the NBUS with respect to BUS and other powerful normality tests are illustrated by means of a Monte Carlo experiment for finite samples. Besides, we show how an adaptation of NBUS for testing departing from normality due only to kurtosis, leads to comparable performances with classical tests based on the fourth moment. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • 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.
    • Handle: RePEc:spr:stmapp:v:24:y:2015:i:3:p:449-474
    DOI: 10.1007/s10260-014-0292-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10260-014-0292-5
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10260-014-0292-5?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. Coin, Daniele, 2008. "A goodness-of-fit test for normality based on polynomial regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2185-2198, January.
    2. Aldo Goia & Ernesto Salinelli & Pascal Sarda, 2011. "Exploring the statistical applicability of the Poincaré inequality: a test of normality," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 334-352, August.
    3. 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.
    4. Bonett, Douglas G. & Seier, Edith, 2002. "A test of normality with high uniform power," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 435-445, September.
    5. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    6. Salinelli, Ernesto, 2009. "Nonlinear principal components, II: Characterization of normal distributions," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 652-660, April.
    7. Urzua, Carlos M., 1996. "On the correct use of omnibus tests for normality," Economics Letters, Elsevier, vol. 53(3), pages 247-251, December.
    8. Poitras, Geoffrey, 2006. "More on the correct use of omnibus tests for normality," Economics Letters, Elsevier, vol. 90(3), pages 304-309, March.
    9. Carota, Cinzia, 2010. "Tests for normality in classes of skew-t alternatives," Statistics & Probability Letters, Elsevier, vol. 80(1), pages 1-8, January.
    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. Norbert Henze & Stefan Koch, 2020. "On a test of normality based on the empirical moment generating function," Statistical Papers, Springer, vol. 61(1), pages 17-29, February.

    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. Jurgita Arnastauskaitė & Tomas Ruzgas & Mindaugas Bražėnas, 2021. "An Exhaustive Power Comparison of Normality Tests," Mathematics, MDPI, vol. 9(7), pages 1-20, April.
    2. Coin, Daniele, 2008. "A goodness-of-fit test for normality based on polynomial regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2185-2198, January.
    3. Hui, Wallace & Gel, Yulia R. & Gastwirth, Joseph L., 2008. "lawstat: An R Package for Law, Public Policy and Biostatistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i03).
    4. Shalit, Haim, 2012. "Using OLS to test for normality," Statistics & Probability Letters, Elsevier, vol. 82(11), pages 2050-2058.
    5. repec:jss:jstsof:28:i03 is not listed on IDEAS
    6. Thorsten Thadewald & Herbert Buning, 2007. "Jarque-Bera Test and its Competitors for Testing Normality - A Power Comparison," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(1), pages 87-105.
    7. Gel, Yulia R. & Miao, Weiwen & Gastwirth, Joseph L., 2007. "Robust directed tests of normality against heavy-tailed alternatives," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2734-2746, February.
    8. Thadewald, Thorsten & Büning, Herbert, 2004. "Jarque-Bera test and its competitors for testing normality: A power comparison," Discussion Papers 2004/9, Free University Berlin, School of Business & Economics.
    9. Charalampos Basdekis & Apostolos Christopoulos & Alexandros Gkolfinopoulos & Ioannis Katsampoxakis, 2022. "VaR as a risk management framework for the spot and futures tanker markets," Operational Research, Springer, vol. 22(4), pages 4287-4352, September.
    10. Wuertz, Diethelm & Katzgraber, Helmut, 2009. "Precise finite-sample quantiles of the Jarque-Bera adjusted Lagrange multiplier test," MPRA Paper 19155, University Library of Munich, Germany.
    11. King, Maxwell L. & Zhang, Xibin & Akram, Muhammad, 2020. "Hypothesis testing based on a vector of statistics," Journal of Econometrics, Elsevier, vol. 219(2), pages 425-455.
    12. Urzúa, Carlos M., 1996. "Omnibus Tests for Multivariate Normality of Observations and Residuals," EGAP Working Papers 200304, Tecnológico de Monterrey, Campus Ciudad de México.
    13. Aldo Goia & Ernesto Salinelli & Pascal Sarda, 2011. "Exploring the statistical applicability of the Poincaré inequality: a test of normality," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 334-352, August.
    14. Kim, Namhyun, 2016. "A robustified Jarque–Bera test for multivariate normality," Economics Letters, Elsevier, vol. 140(C), pages 48-52.
    15. Poitras, Geoffrey, 2006. "More on the correct use of omnibus tests for normality," Economics Letters, Elsevier, vol. 90(3), pages 304-309, March.
    16. Timo Kuosmanen & Mogens Fosgerau, 2009. "Neoclassical versus Frontier Production Models? Testing for the Skewness of Regression Residuals," Scandinavian Journal of Economics, Wiley Blackwell, vol. 111(2), pages 351-367, June.
    17. Islam, Tanweer ul, 2008. "Normality Testing- A New Direction," MPRA Paper 16452, University Library of Munich, Germany.
    18. Shigekazu Nakagawa & Hiroki Hashiguchi & Naoto Niki, 2012. "Improved omnibus test statistic for normality," Computational Statistics, Springer, vol. 27(2), pages 299-317, June.
    19. Zanini, Fabio C. & Irwin, Scott H. & Schnitkey, Gary D. & Sherrick, Bruce J., 2000. "Estimating Farm-Level Yield Distributions For Corn And Soybeans In Illinois," 2000 Annual meeting, July 30-August 2, Tampa, FL 21720, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    20. Bierens, H.J. & Broersma, L., 1991. "The relation between unemployment and interest rate : some international evidence," Serie Research Memoranda 0112, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    21. Juncal Cunado & David Gabauer & Rangan Gupta, 2024. "Realized volatility spillovers between energy and metal markets: a time-varying connectedness approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-17, December.

    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:24:y:2015:i:3:p:449-474. 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.