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Biometrika 2002 89(1):230-237; doi:10.1093/biomet/89.1.230
© 2002 by Biometrika Trust
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Miscellaneous

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

J.Chen1, R.R. Sitter2 and C.Wu3

1 Department of Statistics & Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1 jhchen{at}uwaterloo.ca 2 Department of Statistics & Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada, V 5A 1S6 sitter{at}stat.sfu.ca 3 Department of Statistics & Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1 cbwu{at}uwaterloo.ca

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.

Key Words: Benchmarking; Model calibration; Newton–Raphson

Received July 2000. Revised August 2001


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