The influence in comparing covariance matrices
The influence measure for the likelihood ratio test for comparing two covariance matrices is derived using the influence curve approach under the normality assumption. The influence measure for testing the equality of covariance matrices against the arbitrariness of them is partitioned into three in...
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Veröffentlicht in: | Communications in statistics. Theory and methods 1995-01, Vol.24 (6), p.1431-1441 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | The influence measure for the likelihood ratio test for comparing two covariance matrices is derived using the influence curve approach under the normality assumption. The influence measure for testing the equality of covariance matrices against the arbitrariness of them is partitioned into three influence measures: one for testing the equality of covariance matrices against the proportionality of them, another for testing the proportionality against the equality of correlations between them and the other for testing the equality of correlations against the arbitrariness. This partition implies that an observation can be influential in performing some tests among the four tests but not in performing the remaining tests. Thus the partition is more informative than considering the influence measure for the test of equality alone. Each influence measure is useful for detecting outliers in performing the corresponding likelihood ratio test. |
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ISSN: | 0361-0926 1532-415X |
DOI: | 10.1080/03610929508831563 |