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Bayesian semiparametric regression [PDF]

, 2012
We consider Bayesian estimation of restricted conditional moment models with linear regression as a particular example. The standard practice in the Bayesian literature for semiparametric models is to use flexible families of distributions for the errors and assume that the errors are independent from covariates.
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Introduction to Semiparametric Methods

Journal of Statistical Theory and Practice, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kedem, Benjamin, Lu, Guanhua
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Semiparametric Regression

2003
Semiparametric regression is concerned with the flexible incorporation of non-linear functional relationships in regression analyses. Any application area that benefits from regression analysis can also benefit from semiparametric regression. Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the ...
David Ruppert, M. P. Wand, R. J. Carroll
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Semiparametric regression model selections

Journal of Statistical Planning and Inference, 1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shi, Peide, Tsai, Chih-Ling
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Semiparametric econometrics: A survey

Journal of Applied Econometrics, 1988
AbstractSemiparametric econometric models contain both parametric and nonparametric components, reflecting in some fashion what has been learned from economic theory and previous empirical experience, and what remains unknown. They raise such questions as how well the parametric component can be estimated, and how to construct rules of inference with ...
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Semiparametric Families

2007
Albert W. Marshall, Ingram Olkin
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Semiparametric Regression

Journal of the American Statistical Association, 2006
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Semiparametrics

1996
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Semiparametric Models

2001
Joseph G. Ibrahim, Ming-Hui Chen, Debajyoti Sinha   +2 more
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Semiparametric Methods

1995
R. J. Carroll, D. Ruppert, L. A. Stefanski   +2 more
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