TY - JOUR
T1 - Sufficient dimension reduction in regressions across heterogeneous subpopulations
AU - Ni, Liqiang
AU - Dennis Cook, R.
PY - 2006/2
Y1 - 2006/2
N2 - Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromonte and co-workers extended this method to regressions with qualitative predictors and developed a method, partial sliced inverse regression, under the assumption that the covariance matrices of the continuous predictors are constant across the levels of the qualitative predictor. We extend partial sliced inverse regression by removing the restrictive homogeneous covariance condition. This extension, which significantly expands the applicability of the previous methodology, is based on a new estimation method that makes use of a non-linear least squares objective function.
AB - Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromonte and co-workers extended this method to regressions with qualitative predictors and developed a method, partial sliced inverse regression, under the assumption that the covariance matrices of the continuous predictors are constant across the levels of the qualitative predictor. We extend partial sliced inverse regression by removing the restrictive homogeneous covariance condition. This extension, which significantly expands the applicability of the previous methodology, is based on a new estimation method that makes use of a non-linear least squares objective function.
KW - General partial sliced inverse regression
KW - Partial sliced inverse regression
KW - Sliced inverse regression
KW - Sufficient dimension reduction
UR - http://www.scopus.com/inward/record.url?scp=33645024090&partnerID=8YFLogxK
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U2 - 10.1111/j.1467-9868.2005.00534.x
DO - 10.1111/j.1467-9868.2005.00534.x
M3 - Article
AN - SCOPUS:33645024090
SN - 1369-7412
VL - 68
SP - 89
EP - 107
JO - Journal of the Royal Statistical Society. Series B: Statistical Methodology
JF - Journal of the Royal Statistical Society. Series B: Statistical Methodology
IS - 1
ER -