Editing and Imputation for Quantitative Survey Data

1 March 1987

journal article
research article
Published by JSTOR in Journal of the American Statistical Association

Vol. 82 (397) , 58
https://doi.org/10.2307/2289125

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.

Keywords

SWEEP OPERATOR
EM ALGORITHM
GRAPHICAL METHODS
INCOMPLETE DATA
MAHALANOBIS DISTANCE
MAXIMUM LIKELIHOOD ESTIMATION
MULTIVARIATE DATA
OUTLIER DETECTION
ROBUST ESTIMATION

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