Baysian Quantile Regression
Recent work by Schennach (2005) has opened the way to a Bayesian treatment of quantile regression. Her method, called Bayesian exponentially tilted empirical likelihood (BETEL), provides a likelihood for data y subject only to a set of m moment conditions of the form Eg(y, ?) = 0 where ? is a k dimensional parameter of interest and k may be smaller, equal to or larger than m. The method may be thought of as construction of a likelihood supported on the n data points that is minimally informative, in the sense of maximum entropy, subject to the moment conditions.
(This abstract was borrowed from another version of this item.)
|Date of creation:||2006|
|Contact details of provider:|| Postal: Department of Economics, Brown University, Providence, RI 02912|
References listed on IDEAS
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- Chamberlain, Gary & Imbens, Guido W, 2003.
"Nonparametric Applications of Bayesian Inference,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 21(1), pages 12-18, January.
- Gary Chamberlain & Guido W. Imbens, 1996. "Nonparametric Applications of Bayesian Inference," Harvard Institute of Economic Research Working Papers 1772, Harvard - Institute of Economic Research.
- Imbens, Guido & Chamberlain, Gary, 1996. "Nonparametric Applications of Bayesian Inference," Scholarly Articles 3221493, Harvard University Department of Economics.
- Gary Chamberlain & Guido W. Imbens, 1996. "Nonparametric Applications of Bayesian Inference," NBER Technical Working Papers 0200, National Bureau of Economic Research, Inc.
- Susanne M. Schennach, 2005. "Bayesian exponentially tilted empirical likelihood," Biometrika, Biometrika Trust, vol. 92(1), pages 31-46, March.
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