Contour regression: A general approach to dimension reduction

@misc{citeulike:698704, abstract = {We propose a novel approach to sufficient dimension reduction in regression, based on estimating contour directions of small variation in the response. These directions span the orthogonal complement of the minimal space relevant for the regression and can be extracted according to two measures of variation in the response, leading to simple and general contour regression (SCR and GCR) methodology. In comparison with existing sufficient dimension reduction techniques, this contour-based methodology guarantees exhaustive estimation of the central subspace under ellipticity of the predictor distribution and mild additional assumptions, while maintaining \sqrtn-consistency and computational ease. Moreover, it proves robust to departures from ellipticity. We establish population properties for both SCR and GCR, and asymptotic properties for SCR. Simulations to compare performance with that of standard techniques such as ordinary least squares, sliced inverse regression, principal Hessian directions and sliced average variance estimation confirm the advantages anticipated by the theoretical analyses. We demonstrate the use of contour-based methods on a data set concerning soil evaporation.}, author = {Li, Bing and Zha, Hongyuan and Chiaromonte, Francesca }, citeulike-article-id = {698704}, eprint = {math.ST/0508277}, journal = {Annals of Statistics}, keywords = {dimension-reduction}, month = {Aug}, pages = {1580--1616}, posted-at = {2006-06-16 22:10:53}, priority = {2}, title = {Contour regression: A general approach to dimension reduction}, url = {http://arxiv.org/abs/math.ST/0508277}, volume = {33}, year = {2005} }

TY - ELEC ID - citeulike:698704 L3 - citeulike-article-id:698704 TI - Contour regression: A general approach to dimension reduction JF - Annals of Statistics VL - 33 SP - 1580 EP - 1616 N2 - We propose a novel approach to sufficient dimension reduction in regression, based on estimating contour directions of small variation in the response. These directions span the orthogonal complement of the minimal space relevant for the regression and can be extracted according to two measures of variation in the response, leading to simple and general contour regression (SCR and GCR) methodology. In comparison with existing sufficient dimension reduction techniques, this contour-based methodology guarantees exhaustive estimation of the central subspace under ellipticity of the predictor distribution and mild additional assumptions, while maintaining \sqrtn-consistency and computational ease. Moreover, it proves robust to departures from ellipticity. We establish population properties for both SCR and GCR, and asymptotic properties for SCR. Simulations to compare performance with that of standard techniques such as ordinary least squares, sliced inverse regression, principal Hessian directions and sliced average variance estimation confirm the advantages anticipated by the theoretical analyses. We demonstrate the use of contour-based methods on a data set concerning soil evaporation. KW - dimension-reduction AU - Li, Bing AU - Zha, Hongyuan AU - Chiaromonte, Francesca PY - 2005/Aug/16/ UR - http://arxiv.org/abs/math.ST/0508277 ER -