Bayesian variants of some classical semiparametric regression techniques
AbstractThis paper develops new Bayesian methods for semiparametric inference in the partial linear Normal regression model. These methodes draw solely on teh Normal linear regression model with natural conjugate prior. Hence, analytical finite sample results are available which do not suffer form problems of theoretical and computational complexity which plague the existing literature.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 123 (2004)
Issue (Month): 2 (December)
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Web page: http://www.elsevier.com/locate/jeconom
Other versions of this item:
- Koop, G. & Poirier, D., 2000. "Bayesian Variants of Some Classical Semiparametric Regression Techniques," Papers, California Irvine - School of Social Sciences 00-01-22, California Irvine - School of Social Sciences.
- Gary Koop & Dale J. Poirer, 2004. "Bayesian Variants of Some classical Semiparametric Regression Techniques," ESE Discussion Papers 73, Edinburgh School of Economics, University of Edinburgh.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
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