Consistent estimation for the non-normal ultrastructural model
AbstractThe present article considers the linear ultrastructural model which encompasses the two popular forms of measurement error models, viz., the functional and structural models. The measurement error variance associated with the explanatory variable is assumed to be known which leads to consistent estimators of parameters. The efficiency properties of the thus obtained estimators of slope parameter, intercept term and the measurement error variance of study variable are derived under non-normal error distributions and the effects of departures from symmetry and peakedness of the error distributions are studied.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 34 (1997)
Issue (Month): 1 (May)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Shalabh & Garg, Gaurav & Misra, Neeraj, 2009. "Use of prior information in the consistent estimation of regression coefficients in measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1498-1520, August.
- Schneeweiss, H. & Shalabh, H., 2007. "On the estimation of the linear relation when the error variances are known," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 1143-1148, October.
- Shalabh & Garg, Gaurav & Misra, Neeraj, 2007. "Restricted regression estimation in measurement error models," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 1149-1166, October.
- Shalabh & Gaurav Garg & Neeraj Misra, 2010. "Consistent estimation of regression coefficients in ultrastructural measurement error model using stochastic prior information," Statistical Papers, Springer, vol. 51(3), pages 717-748, September.
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