Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveys.

CHEN, J.; SITTER, R. R.; WU, C.

Design weights in surveys are often adjusted to accommodate auxiliary information and to meet pre‐specified range restrictions, typically via some ad hoc algorithmic adjustment to a generalised regression estimator. In this paper, we present a simple solution to this problem using empirical likelihood methods or generalised regression. We first develop algorithms for computing empirical likelihood estimators and model‐calibrated empirical likelihood estimators. The first algorithm solves the computational problem of the empirical likelihood method in general, both in survey and non‐survey settings, and theoretically guarantees its convergence. The second exploits properties of the model‐calibration method and is particularly simple. The algorithms are adapted for handling benchmark constraints and pre‐specified range restrictions on the weight adjustments.

REGRESSION analysis; ESTIMATION theory; BENCHMARKING (Management); NEWTON-Raphson method; ALGORITHMS

Biometrika, 2002, Vol 89, Issue 1, p230

0006-3444

Academic Journal

10.1093/biomet/89.1.230