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Likelihood-based inference for power distributions

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Author Info

  • Arthur Pewsey

    ()

  • Héctor Gómez

    ()

  • Heleno Bolfarine

    ()

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    Abstract

    This paper considers likelihood-based inference for the family of power distributions. Widely applicable results are presented which can be used to conduct inference for all three parameters of the general location-scale extension of the family. More specific results are given for the special case of the power normal model. The analysis of a large data set, formed from density measurements for a certain type of pollen, illustrates the application of the family and the results for likelihood-based inference. Throughout, comparisons are made with analogous results for the direct parametrisation of the skew-normal distribution. Copyright Sociedad de Estadística e Investigación Operativa 2012

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    File URL: http://hdl.handle.net/10.1007/s11749-011-0280-0
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    Bibliographic Info

    Article provided by Springer in its journal TEST.

    Volume (Year): 21 (2012)
    Issue (Month): 4 (December)
    Pages: 775-789

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    Handle: RePEc:spr:testjl:v:21:y:2012:i:4:p:775-789

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    Related research

    Keywords: Generalised Gaussian distribution; Kurtosis; Lehmann alternatives; Power normal model; Skew-normal distribution; Skewness; 60E05; 62F10; 62F12;

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    References

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    1. Rameshwar Gupta & Ramesh Gupta, 2008. "Analyzing skewed data by power normal model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 17(1), pages 197-210, May.
    2. Barry Arnold & Robert Beaver & Richard Groeneveld & William Meeker, 1993. "The nontruncated marginal of a truncated bivariate normal distribution," Psychometrika, Springer, vol. 58(3), pages 471-488, September.
    3. Arthur Pewsey, 2000. "Problems of inference for Azzalini's skewnormal distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(7), pages 859-870.
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