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December 1996 On nonparametric estimation of intercept and slope distributions in random coefficient regression
Rudolf Beran, Andrey Feuerverger, Peter Hall
Ann. Statist. 24(6): 2569-2592 (December 1996). DOI: 10.1214/aos/1032181170

Abstract

An experiment records stimulus and response for a random sample of cases. The relationship between response and stimulus is thought to be linear, the values of the slope and intercept varying by case. From such data, we construct a consistent, asymptotically normal, nonparametric estimator for the joint density of the slope and intercept. Our methodology incorporates the radial projection-slice theorem for the Radon transform, a technique for locally linear nonparametric regression and a tapered Fourier inversion. Computationally, the new density estimator is more feasible than competing nonparametric estimators, one of which is based on moments and the other on minimum distance considerations.

Citation

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Rudolf Beran. Andrey Feuerverger. Peter Hall. "On nonparametric estimation of intercept and slope distributions in random coefficient regression." Ann. Statist. 24 (6) 2569 - 2592, December 1996. https://doi.org/10.1214/aos/1032181170

Information

Published: December 1996
First available in Project Euclid: 16 September 2002

zbMATH: 0867.62021
MathSciNet: MR1425969
Digital Object Identifier: 10.1214/aos/1032181170

Subjects:
Primary: 62G07
Secondary: 62J05

Rights: Copyright © 1996 Institute of Mathematical Statistics

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Vol.24 • No. 6 • December 1996
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