This paper proposes the asymmetric rim weighting algorithm as an alternative to rim weighting (also called raking). The latter is currently a popular method for grossing up the results of a sample survey, but asymmetric rim weighting produces results that are more efficient and have fewer high weights, with little or no increase in processing time.
BrickJ.M., & KaltonG. (1996) Handling missing data in survey research. Statistical Methods in Medical Research, 5, pp. 215–238.
3.
ConwayS. (1982) The weighting game. Market Research Society Conference Papers, pp 193–207.
4.
DemingW.E., & StephanF.F. (1940) On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. Annals of Mathematical Statistics, 11, pp. 427–444.
5.
IrelandC.T., & KullbackS. (1968) Contingency tables with given marginals. Biometrika, 55, pp. 179–188.
6.
KaltonG., & Flores-CervantesI. (2003) Weighting methods. Journal of Official Statistics, 19, 2, pp. 81–97.
7.
KishL. (1992) Weighting for unequal Pi. Journal of Official Statistics, 8, pp. 183–200.
8.
MoserC., & KaltonG. (1971) Survey Methods in Social Investigation.London: Heinemann Educational.
9.
PotterF.J. (1988) Survey of procedures to control extreme sampling weights. Proceedings of the American Statistical Association, Section on Survey Research Methods, pp. 453–458.
10.
PotterF.J. (1990) A study of procedures to identify and trim extreme sampling weights. Proceedings of the American Statistical Association, Section on Survey Research Methods, pp. 225–230.
11.
PotterF.J. (1993) The effect of weight trimming on nonlinear survey estimates. Proceedings of the American Statistical Association, Section on Survey Research Methods, pp. 758–763.
12.
SharotT. (1986) Weighting survey results. Journal of the Market Research Society, 28, 3, pp. 269–284.
