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Bartlett Corrections for One-Parameter Exponential Family Models

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Author Info

  • G.M. Cordeiro

    (Univ. Federal de Pernambuco)

  • F. Cribari-Neto

    (Southern Illinois Univ. at Carbondale)

  • E.C.Q. Aubin

    (Univ. de Sao Paulo)

  • S.L.P. Ferrari

    (Univ. de Sao Paulo)

Registered author(s):

    Abstract

    In this paper we derive a general closed-form expression for the Bartlett correction for the test of H_0: \theta= \theta**(0), where "theta is a scalar parameter of a one-parameter exponential family model. Our results are general enough to cover many important and commonly used distributions. Several special cases and classes of variance functions of considerable importance are discussed, and some approximations based on asymptotic expansions are given. We also use a graphical analysis to examine how the correction varies with \theta in some special cases. Simulation results are also given.

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    File URL: http://128.118.178.162/eps/em/papers/9506/9506001.pdf
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    File URL: http://128.118.178.162/eps/em/papers/9506/9506001.ps.gz
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    Bibliographic Info

    Paper provided by EconWPA in its series Econometrics with number 9506001.

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    Length: 20 pages
    Date of creation: 01 Jun 1995
    Date of revision:
    Handle: RePEc:wpa:wuwpem:9506001

    Note: 20 pages; 10 self-contained figures and 3 tables; written with an implementation of TeX; single PostScript file FTP'ed. E-mail to Francisco Cribari-Neto (cribari @ c22c.c-wham.siu.edu).
    Contact details of provider:
    Web page: http://128.118.178.162

    Related research

    Keywords: Bartlett correction; chi-squared distribution; exponential family; likelihood ratio statistic;

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    References

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    1. Cordeiro, Gauss M., 1993. "General matrix formulae for computing Bartlett corrections," Statistics & Probability Letters, Elsevier, vol. 16(1), pages 11-18, January.
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    Citations

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    Cited by:
    1. Ferrari, Silvia L. P. & Cordeiro, Gauss M. & Uribe-Opazo, Miguel A. & Cribari-Neto, Francisco, 1996. "Improved score tests for one-parameter exponential family models," Statistics & Probability Letters, Elsevier, vol. 30(1), pages 61-71, September.

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