The sliced inverse regression algorithm as a maximum likelihood procedure

It is shown that the sliced inverse regression procedure proposed by Li corresponds to the maximum likelihood estimate where the observations in each slice are samples of multivariate normal distributions with means in an affine manifold. © 2009 Elsevier B.V. All rights reserved.

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Detalles Bibliográficos
Autores principales:	Szretter, M.E., Yohai, V.J.
Formato:	JOUR
Materias:	Central mean subspace Maximum likelihood estimates Sliced inverse regression
Acceso en línea:	http://hdl.handle.net/20.500.12110/paper_03783758_v139_n10_p3570_Szretter
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	todo:paper_03783758_v139_n10_p3570_Szretter
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spelling	todo:paper_03783758_v139_n10_p3570_Szretter2023-10-03T15:32:16Z The sliced inverse regression algorithm as a maximum likelihood procedure Szretter, M.E. Yohai, V.J. Central mean subspace Maximum likelihood estimates Sliced inverse regression It is shown that the sliced inverse regression procedure proposed by Li corresponds to the maximum likelihood estimate where the observations in each slice are samples of multivariate normal distributions with means in an affine manifold. © 2009 Elsevier B.V. All rights reserved. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_03783758_v139_n10_p3570_Szretter
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Central mean subspace Maximum likelihood estimates Sliced inverse regression
spellingShingle	Central mean subspace Maximum likelihood estimates Sliced inverse regression Szretter, M.E. Yohai, V.J. The sliced inverse regression algorithm as a maximum likelihood procedure
topic_facet	Central mean subspace Maximum likelihood estimates Sliced inverse regression
description	It is shown that the sliced inverse regression procedure proposed by Li corresponds to the maximum likelihood estimate where the observations in each slice are samples of multivariate normal distributions with means in an affine manifold. © 2009 Elsevier B.V. All rights reserved.
format	JOUR
author	Szretter, M.E. Yohai, V.J.
author_facet	Szretter, M.E. Yohai, V.J.
author_sort	Szretter, M.E.
title	The sliced inverse regression algorithm as a maximum likelihood procedure
title_short	The sliced inverse regression algorithm as a maximum likelihood procedure
title_full	The sliced inverse regression algorithm as a maximum likelihood procedure
title_fullStr	The sliced inverse regression algorithm as a maximum likelihood procedure
title_full_unstemmed	The sliced inverse regression algorithm as a maximum likelihood procedure
title_sort	sliced inverse regression algorithm as a maximum likelihood procedure
url	http://hdl.handle.net/20.500.12110/paper_03783758_v139_n10_p3570_Szretter
work_keys_str_mv	AT szretterme theslicedinverseregressionalgorithmasamaximumlikelihoodprocedure AT yohaivj theslicedinverseregressionalgorithmasamaximumlikelihoodprocedure AT szretterme slicedinverseregressionalgorithmasamaximumlikelihoodprocedure AT yohaivj slicedinverseregressionalgorithmasamaximumlikelihoodprocedure
_version_	1782030476804882432

The sliced inverse regression algorithm as a maximum likelihood procedure

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