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DETECTING INFLUENTIAL OBSERVATIONS IN SLICED INVERSE REGRESSION ANALYSIS
Author(s) -
Prendergast Luke A.
Publication year - 2006
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/j.1467-842x.2006.00441.x
Subject(s) - sliced inverse regression , mathematics , sufficient dimension reduction , dimensionality reduction , statistics , dimension (graph theory) , subspace topology , inverse , regression , measure (data warehouse) , regression analysis , linear regression , econometrics , artificial intelligence , data mining , computer science , combinatorics , mathematical analysis , geometry
Summary The detection of influential observations on the estimation of the dimension reduction subspace returned by Sliced Inverse Regression (SIR) is considered. Although there are many measures to detect influential observations in related methods such as multiple linear regression, there has been little development in this area with respect to dimension reduction. One particular influence measure for a version of SIR is examined and it is shown, via simulation and example, how this may be used to detect influential observations in practice.