|
Activity Number:
|
57
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Sunday, August 6, 2006 : 4:00 PM to 5:50 PM
|
|
Sponsor:
|
Section on Survey Research Methods
|
| Abstract - #306741 |
|
Title:
|
Local Polynomial Regression for Small-Area Estimation
|
|
Author(s):
|
Pushpal Mukhopadhyay*+ and Tapabrata Maiti
|
|
Companies:
|
Iowa State University and Iowa State University
|
|
Address:
|
204 Snedecor Hall, Ames, IA, 50010,
|
|
Keywords:
|
nonparametric ; multi-level modeling ; James-Stein estimator ; area level small area model
|
|
Abstract:
|
Estimation of small-area means in the presence of area-level auxiliary information is considered. A class of estimators based on local polynomial regression is proposed. The assumptions on the area-level regression are considerably weaker than standard small-area models. Both the small-area mean function and the between-area variance function are modeled as smooth functions of the area-level covariates. A composite estimator that is a convex combination of the design-weighted mean and the prediction from the nonparametric model is developed. The estimator is shown to be asymptotically consistent under mild regularity conditions. An approximation of the mean squared error (MSE), based on Taylor linearization, and an estimate of the approximate MSE are developed and their theoretical properties studied.
|
