Sufficient dimension reduction in regressions across heterogeneous subpopulations
Abbreviated Journal Title
J. R. Stat. Soc. Ser. B-Stat. Methodol.
general partial sliced inverse regression; partial sliced inverse; regression; sliced inverse regression; sufficient dimension reduction; SLICED INVERSE REGRESSION; SQUARES; MODELS; Statistics & Probability
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.
Journal of the Royal Statistical Society Series B-Statistical Methodology
"Sufficient dimension reduction in regressions across heterogeneous subpopulations" (2006). Faculty Bibliography 2000s. 6471.