Implications of influence function analysis for sliced inverse regression and sliced average variance estimation

Sliced inverse regression, sliced inverse regression II and sliced average variance estimation are three related dimension-reduction methods that require relatively mild model assumptions. As an approximation for the relative influence of single observations from large samples, the influence functio...

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Published inBiometrika Vol. 94; no. 3; pp. 585 - 601
Main Author Prendergast, Luke A
Format Journal Article
LanguageEnglish
Published Oxford University Press for Biometrika Trust 01.08.2007
SeriesBiometrika
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Abstract Sliced inverse regression, sliced inverse regression II and sliced average variance estimation are three related dimension-reduction methods that require relatively mild model assumptions. As an approximation for the relative influence of single observations from large samples, the influence function is used to compare the sensitivity of the three methods to particular observational types. The analysis carried out here helps to explain why there is a lack of agreement concerning the preferability of these dimension-reduction procedures in general. An efficient sample version of the influence function is also developed and evaluated. Copyright 2007, Oxford University Press.
AbstractList Sliced inverse regression, sliced inverse regression II and sliced average variance estimation are three related dimension-reduction methods that require relatively mild model assumptions. As an approximation for the relative influence of single observations from large samples, the influence function is used to compare the sensitivity of the three methods to particular observational types. The analysis carried out here helps to explain why there is a lack of agreement concerning the preferability of these dimension-reduction procedures in general. An efficient sample version of the influence function is also developed and evaluated. Copyright 2007, Oxford University Press.
Author Prendergast, Luke A
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Title Implications of influence function analysis for sliced inverse regression and sliced average variance estimation
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