Nonparametric Applications of Bayesian Inference
AbstractThis article evaluates the usefulness of a nonparametric approach to Bayesian inference by presenting two applications. Our first application considers an educational choice problem. We focus on obtaining a predictive distribution for earnings corresponding to various levels of schooling. This predictive distribution incorporates the parameter uncertainty, so that it is relevant for decision making under uncertainty in the expected utility framework of microeconomics. The second application is to quantile regression. Our point here is to examine the potential of the nonparametric framework to provide inferences without relying on asymptotic approximations. Unlike in the first application, the standard asymptotic normal approximation turns out not to be a good guide.
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Bibliographic InfoArticle provided by American Statistical Association in its journal Journal of Business and Economic Statistics.
Volume (Year): 21 (2003)
Issue (Month): 1 (January)
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Other versions of this item:
- Gary Chamberlain & Guido W. Imbens, 1996. "Nonparametric Applications of Bayesian Inference," Harvard Institute of Economic Research Working Papers 1772, Harvard - Institute of Economic Research.
- Gary Chamberlain & Guido W. Imbens, 1996. "Nonparametric Applications of Bayesian Inference," NBER Technical Working Papers 0200, National Bureau of Economic Research, Inc.
- Imbens, Guido & Chamberlain, Gary, 1996. "Nonparametric Applications of Bayesian Inference," Scholarly Articles 3221493, Harvard University Department of Economics.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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