The use of mixtures for dealing with non-normal regression errors
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 48 (2005)
Issue (Month): 4 (April)
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Web page: http://www.elsevier.com/locate/csda
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- Steven Caudill & James Long, 2010. "Do former athletes make better managers? Evidence from a partially adaptive grouped-data regression model," Empirical Economics, Springer, vol. 39(1), pages 275-290, August.
- Maria Karlsson & Thomas Laitila, 2014. "Finite mixture modeling of censored regression models," Statistical Papers, Springer, vol. 55(3), pages 627-642, August.
- Galimberti, Giuliano & Soffritti, Gabriele, 2014. "A multivariate linear regression analysis using finite mixtures of t distributions," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 138-150.
- Katherine G. Yewell & Steven B. Caudill & Franklin G. Mixon, Jr., 2014. "Referee Bias and Stoppage Time in Major League Soccer: A Partially Adaptive Approach," Econometrics, MDPI, Open Access Journal, vol. 2(1), pages 1-19, February.
- Giovanni Mellace & Roberto Rocci, 2011. "Principal Stratification in sample selection problems with non normal error terms," CEIS Research Paper 194, Tor Vergata University, CEIS, revised 02 May 2011.
- Steven Caudill, 2012. "A partially adaptive estimator for the censored regression model based on a mixture of normal distributions," Statistical Methods and Applications, Springer, vol. 21(2), pages 121-137, June.
- Islam, Tanweer ul, 2008. "Normality Testing- A New Direction," MPRA Paper 16452, University Library of Munich, Germany.
- Usta, Ilhan & Kantar, Yeliz Mert, 2011. "On the performance of the flexible maximum entropy distributions within partially adaptive estimation," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2172-2182, June.
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