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  • Search: subject:"semiparametric estimation and inference"
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Year of publication
Subject
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asymptotic and finite sample risk 3 quadratic loss 3 semiparametric estimation and inference 3 Stein-like shrinkage 2 data dependent shrinkage vector 2 ill-conditioned design 2 Cl0 1 Research Methods/ Statistical Methods 1 data dependent shrinkage 1 empirical likelihood procedures 1 endogeneity 1 multinomial process 1
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Online availability
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Free 3
Type of publication
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Book / Working Paper 3
Language
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Undetermined 2 English 1
Author
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Judge, George G. 3 Mittelhammer, Ron C 2 Mittelhammer, Ronald C. 1
Institution
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Department of Agricultural and Resource Economics, University of California-Berkeley 2
Published in...
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Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series 2
Source
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RePEc 2 BASE 1
Showing 1 - 3 of 3
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Estimating the Link Function in Multinomial Response Models under Endogeneity and Quadratic Loss
Judge, George G.; Mittelhammer, Ron C - Department of Agricultural and Resource Economics, … - 2004
This paper considers estimation and inference for the multinomial response model in the case where endogenous variables are arguments of the unknown link function. Semiparametric estimators are proposed that avoid the parametric assumptions underlying the likelihood approach as well as the loss...
Persistent link: https://www.econbiz.de/10010537491
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A Semi-Parametric Basis for Combining Estimation Problems Under Quadratic Loss
Judge, George G.; Mittelhammer, Ronald C. - 2003
When there is uncertainty concerning the appropriate statistical model to use in representing the data sampling process and corresponding estimators, we consider a basis for optimally combining estimation problems. In the context of the multivariate linear statistical model, we consider a...
Persistent link: https://www.econbiz.de/10009442593
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Cover Image
A Semi-Parametric Basis for Combining Estimation Problems Under Quadratic Loss
Judge, George G.; Mittelhammer, Ron C - Department of Agricultural and Resource Economics, … - 2003
When there is uncertainty concerning the appropriate statistical model to use in representing the data sampling process and corresponding estimators, we consider a basis for optimally combining estimation problems. In the context of the multivariate linear statistical model, we consider a...
Persistent link: https://www.econbiz.de/10010537488
Saved in:
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