Using intraslice covariances for improved estimation of the central subspace in regression
Abbreviated Journal Title
inverse regression estimation; sliced inverse regression; sufficient; dimension reduction; SUFFICIENT DIMENSION REDUCTION; SLICED INVERSE REGRESSION; Biology; Mathematical & Computational Biology; Statistics & Probability
Popular methods for estimating the central subspace in regression require slicing a continuous response. However, slicing can result in loss of information and in some cases that loss can be substantial. We use intraslice covariances to construct improved inference methods for the central subspace. These methods are optimal within a class of quadratic inference functions and permit chi-squared tests of conditional independence hypotheses involving the predictors. Our experience gained through simulation is that the new method is never worse than existing methods, and can be substantially better.
"Using intraslice covariances for improved estimation of the central subspace in regression" (2006). Faculty Bibliography 2000s. 6050.