An RKHS formulation of the inverse regression dimension-reduction problem
| dc.creator | Hsing, Tailen | |
| dc.creator | Ren, Haobo | |
| dc.date | 2009-04-01 | |
| dc.date.accessioned | 2026-07-07T12:58:59Z | |
| dc.date.available | 2026-07-07T12:58:59Z | |
| dc.description | Suppose that $Y$ is a scalar and $X$ is a second-order stochastic process, where $Y$ and $X$ are conditionally independent given the random variables $ξ_1,...,ξ_p$ which belong to the closed span $L_X^2$ of $X$. This paper investigates a unified framework for the inverse regression dimension-reduction problem. It is found that the identification of $L_X^2$ with the reproducing kernel Hilbert space of $X$ provides a platform for a seamless extension from the finite- to infinite-dimensional settings. It also facilitates convenient computational algorithms that can be applied to a variety of models. | |
| dc.description | Published in at http://dx.doi.org/10.1214/07-AOS589 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org) | |
| dc.identifier | https://arxiv.org/abs/0904.0076 | |
| dc.identifier | http://arxiv.org/abs/0904.0076 | |
| dc.identifier | Annals of Statistics 2009, Vol. 37, No. 2, 726-755 | |
| dc.identifier | doi:10.1214/07-AOS589 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/225433 | |
| dc.subject | Statistics Theory | |
| dc.subject | 62H99 (Primary), 62M99 (Secondary) | |
| dc.title | An RKHS formulation of the inverse regression dimension-reduction problem | |
| dc.type | text |