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.
Publié le : 1994-12-14
Classification:
Radon transform,
prediction interval,
distribution estimate,
weak convergence metric,
characteristic function,
nonparametric,
semiparametric,
62G05,
62J05
@article{1176325767,
author = {Beran, R. and Millar, P. W.},
title = {Minimum Distance Estimation in Random Coefficient Regression Models},
journal = {Ann. Statist.},
volume = {22},
number = {1},
year = {1994},
pages = { 1976-1992},
language = {en},
url = {http://dml.mathdoc.fr/item/1176325767}
}
Beran, R.; Millar, P. W. Minimum Distance Estimation in Random Coefficient Regression Models. Ann. Statist., Tome 22 (1994) no. 1, pp. 1976-1992. http://gdmltest.u-ga.fr/item/1176325767/