Likelihood-Based Sufficient Dimension Reduction

Authors
Cook, R. Dennis; Forzani, Liliana Maria
Publication Year
2009
Language
English
Format
article
Status
Published version
Description
We obtain the maximum likelihood estimator of the central subspace under conditional normality of the predictors given the response. Analytically and in simulations we found that our new estimator can preform much better than sliced inverse regression, sliced average variance estimation and directional regression, and that it seems quite robust to deviations from normality.
Fil: Cook, R. Dennis. University of Minnesota; Estados Unidos
Fil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina
Subject
CENTRAL SUBSPACE
DIRECTIONAL REGRESSION
GRASSMANN MANIFOLDS
SLICED AVERAGE VARIANCE ESTIMATION
SLICED INVERSE REGRESSION
Estadística y Probabilidad
Matemáticas
CIENCIAS NATURALES Y EXACTAS
Access level
Restricted access
License
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repository
CONICET Digital (CONICET)
Institution
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identifier
oai:ri.conicet.gov.ar:11336/84065