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Noniterative Least Squares Estimates, Standard Errors and F-Tests for Analyses of Variance with Missing Data

Donald B. Rubin
Journal of the Royal Statistical Society. Series B (Methodological)
Vol. 38, No. 3 (1976), pp. 270-274
Published by: Wiley for the Royal Statistical Society
Stable URL: http://www.jstor.org/stable/2984976
Page Count: 5
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Noniterative Least Squares Estimates, Standard Errors and F-Tests for Analyses of Variance with Missing Data
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Abstract

This article generalizes Rubin's method of least squares estimation of missing values in an analysis of variance. The general method produces not only least squares estimates of all parameters and the residual mean square, but also the correct least squares standard error and t-test for each contrast as well as the least squares sum of squares and F-test for each collection of contrasts. The method is non-iterative and requires only those subroutines designed to handle complete data plus a subroutine to find the inverse of an m × m symmetric matrix, where m is the number of missing values.

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