Imputation and unbiased estimation: Use of centered predictive mean neighborhood method
Singh, A. C., Grau, E. A., & Folsom, R. E. (2004, January). Imputation and unbiased estimation: Use of centered predictive mean neighborhood method. Presented at American Statistical Association Meeting, Section of Survey Research Methods, .
Methods for determining the predictive distribution for multivariate imputation range between two extremes, both of which are commonly employed in practice: a completely parametric model-based approach, and a completely nonparametric approach such as the nearest neighbor hot-deck (NNHD). A semiparametric middle ground between these two extremes is to fit a series of univariate models and construct a neighborhood based on the vector of predictive means. This is what is done under the predictive mean neighborhoods (PMN) method, a generalization of Rubin's (1986) and Little’s (1988) predictive mean matching method. Because thedistribution of donors in the PMN neighborhood may not be centered at the recipient's predictive mean, estimators of population means and totals could be biased. To overcome this problem, we propose amodification to PMN which uses sampling weight calibration techniques such as the GEM (generalized exponential model) method of Folsom and Singh tocenter the empirical distribution from the neighborhood. Empirical results on bias and MSE, based on a simulation study using data from the 2002National Survey on Drug Use and Health, are presented to compare the centered PMN with other methods.