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Multivariate Matching Methods That are Equal Percent Bias Reducing, I: Some Examples
Donald B. Rubin
Vol. 32, No. 1 (Mar., 1976), pp. 109-120
Published by: International Biometric Society
Stable URL: http://www.jstor.org/stable/2529342
Page Count: 12
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Multivariate matching methods are commonly used in the behavioral and medical sciences in an attempt to control bias when randomization is not feasible. Some examples of multivariate matching methods are discussed in Althauser and Rubin  and Cochran and Rubin  but otherwise have received little attention in the literature. Here we present examples of multivariate matching methods that will yield the same percent reduction in bias for each matching variable for a variety of underlying distributions. Eleven distributional cases are considered and for each one, matching methods are described which are equal percent bias reducing. The methods discussed in Section 8 will probably be the most generally applicable in practice. These matching methods are based on the values of the sample best linear discriminant or define distance by the inverse of the sample covariance matrix.
Biometrics © 1976 International Biometric Society