Editing and Imputation for Quantitative Survey Data
提出三阶段策略清理调查数据中的缺失和异常值：检测异常案例、识别异常值、插补缺失或异常值，并用制造业年度调查数据演示方法。
Abstract This article develops a three-stage strategy for cleaning survey data with missing and outlying values: (a) detection of outlying cases, (b) detection of outlying values within outlying cases, and (c) imputation of likely values for missing and/or outlying and edited values. Methodological tools include distance measures, graphical procedures, and maximum likelihood and robust estimation for incomplete multivariate normal data. Data from the Annual Survey of Manufactures (ASM) are used to illustrate the method.