Title: Stabilized Multivariate Tests ‐ the Inclusion of Missing Values
Abstract: Abstract In two recent papers, LÄUTER (1996) and LÄUTER, GLIMM, and KROPF (1996) have proposed a new class of exact tests for multivariate normal data with an inherent structure. These tests are based on the construction of scores that have a spherical distribution. The principle can be adapted to a variety of different situations, and it supports exact tests with small samples of high‐dimensional data. In this paper, an enhancement of that theory will be given regarding the problem of missing data. It provides a new mathematical understanding of mean imputation techniques, and it is also the basis for a modified principal component missing data algorithm described here.
Publication Year: 1997
Publication Date: 1997-01-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 3
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