The use of mixtures for dealing with non-normal regression errors
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- Maria Karlsson & Thomas Laitila, 2014. "Finite mixture modeling of censored regression models," Statistical Papers, Springer, vol. 55(3), pages 627-642, August.
- Hu, Hao & Yao, Weixin & Wu, Yichao, 2017. "The robust EM-type algorithms for log-concave mixtures of regression models," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 14-26.
- 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.
- repec:eee:csdana:v:115:y:2017:i:c:p:136-154 is not listed on IDEAS
- 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.
- 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.
- 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.
- Steven Caudill, 2012. "A partially adaptive estimator for the censored regression model based on a mixture of normal distributions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(2), pages 121-137, June.
- 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.
- Islam, Tanweer ul, 2008. "Normality Testing- A New Direction," MPRA Paper 16452, University Library of Munich, Germany.
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