LARGE-SAMPLE SIGNIFICANCE LEVELS FROM MULTIPLY IMPUTED DATA USING MOMENT-BASED STATISTICS AND AN F-REFERENCE DISTRIBUTION

• 成果类型: Article
• 署名作者: LI, KH; RAGHUNATHAN, TE; RUBIN, DB
• 署名单位: Harvard University; University of Washington; University of Washington Seattle
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2290525
• 发表日期: 1991
• 页码: 1065-1073
• 关键词: IMPUTATION

摘要

We present a procedure for computing significance levels from data sets whose missing values have been multiply imputed data. This procedure uses moment-based statistics, m greater-than-or-equal-to 3 repeated imputations, and an F reference distribution. When m = infinity, we show first that our procedure is essentially the same as the ideal procedure in cases of practical importance and, second, that its deviations from the ideal are basically a function of the coefficient of variation of the canonical ratios of complete to observed information. For small m our procedure's performance is largely governed by this coefficient of variation and the mean of these ratios. Using simulation techniques with small m, we compare our procedure's actual and nominal large-sample significance levels and conclude that it is essentially calibrated and thus represents a definite improvement over previously available procedures. Furthermore, we compare the large-sample power of the procedure as a function of m and other factors, such as the dimensionality of the estimand and fraction of missing information, to provide guidance on the choice of the number of imputations; generally, we find the loss of power due to small m to be quite modest in cases likely to occur in practice.