Multiple Imputation for Interval Estimation From Simple Random Samples With Ignorable Nonresponse
研究了简单随机样本中针对可忽略无应答的几种多重插补技术，通过分析和蒙特卡洛结果比较了它们对总体均值区间覆盖率的准确性，发现每次缺失值用2次插补在常见情况下已足够准确，且优于单次插补。
Abstract Several multiple imputation techniques are described for simple random samples with ignorable nonresponse on a scalar outcome variable. The methods are compared using both analytic and Monte Carlo results concerning coverages of the resulting intervals for the population mean. Using m = 2 imputations per missing value gives accurate coverages in common cases and is clearly superior to single imputation (m = 1) in all cases. The performances of the methods for various m can be predicted well by linear interpolation in 1/(m — 1) between the results for m = 2 and m = ∞. As a rough guide, to assure coverages of interval estimates within 2% of the nominal level when using the preferred methods, the number of imputations per missing value should increase from 2 to 3 as the nonresponse rate increases from 10% to 60%.