Likelihood-based inference for power distributions
AbstractThis 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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Bibliographic InfoArticle provided by Springer in its journal TEST.
Volume (Year): 21 (2012)
Issue (Month): 4 (December)
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Web page: http://www.springerlink.com/link.asp?id=120411
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- 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.
- 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.
- 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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