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December, 1994 Minimum Distance Estimation in Random Coefficient Regression Models
R. Beran, P. W. Millar
Ann. Statist. 22(4): 1976-1992 (December, 1994). DOI: 10.1214/aos/1176325767


Random coefficient regression models are important in modeling heteroscedastic multivariate linear regression in econometrics. The analysis of panel data is one example. In statistics, the random and mixed effects models of ANOVA, deconvolution models and affine mixture models are all special cases of random coefficient regression. Some inferential problems, such as constructing prediction regions for the modeled response, require a good nonparametric estimator of the unknown coefficient distribution. This paper introduces and studies a consistent nonparametric minimum distance method for estimating the coefficient distribution. Our estimator translates the difficult problem of estimating an inverse Radon transform into a minimization problem.


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R. Beran. P. W. Millar. "Minimum Distance Estimation in Random Coefficient Regression Models." Ann. Statist. 22 (4) 1976 - 1992, December, 1994.


Published: December, 1994
First available in Project Euclid: 11 April 2007

zbMATH: 0828.62031
MathSciNet: MR1329178
Digital Object Identifier: 10.1214/aos/1176325767

Primary: 62G05
Secondary: 62J05

Keywords: Characteristic function , distribution estimate , nonparametric , prediction interval , Radon transform , semiparametric , weak convergence metric

Rights: Copyright © 1994 Institute of Mathematical Statistics


Vol.22 • No. 4 • December, 1994
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