On nonparametric estimation of intercept and slope distributions in random coefficient regression
Open Access
- 1 December 1996
- journal article
- Published by Institute of Mathematical Statistics in The Annals of Statistics
- Vol. 24 (6) , 2569-2592
- https://doi.org/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.Keywords
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