A Nonparametric Regression Smoother for Nonnegative Data and its Application in Finite Population Sampling
by
Yogendra P. Chaubey
Concordia University

Nonparametric regression using kernel method, local linear smoothing, splines and so on, has been used recently for estimation of mean and other parameters in finite population sampling by adapting the generalized regression estimator (see Särndal, Swensson and Wretman(1992). Model Assisted Survey Sampling. Springer-Verlag, New York.) as in Chaubey and Crisalli (2001)(Technical Report, Department of Mathematics and Statistics, Concordia University, 2001) and Breidt and Opsomer, (2000), (Annals of Statistics, 28, 1026-1053). These methods are general purpose methods and there may be scope for better methods in specific situations. In this paper we employ recently developed estimator for the regression function for non-negative data in Chaubey, Sen and Zhou (2002) in proposing non-parametric regression estimator for estimation of mean and other parameters from a finite population for non-negative data.

Date received: October 2, 2002

Copyright © 2002 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # cais-29.