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Abstract:

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.

Registro:

Documento: Artículo
Título:The sliced inverse regression algorithm as a maximum likelihood procedure
Autor:Szretter, M.E.; Yohai, V.J.
Filiación:Universidad de Buenos Aires, Argentina
Departamento de Matemáticas, Universidad de Buenos Aires, CONICET, Pabellon I-Ciudad Universitaria, 1428 Buenos Aires, Argentina
Palabras clave:Central mean subspace; Maximum likelihood estimates; Sliced inverse regression
Año:2009
Volumen:139
Número:10
Página de inicio:3570
Página de fin:3578
DOI: http://dx.doi.org/10.1016/j.jspi.2009.04.008
Título revista:Journal of Statistical Planning and Inference
Título revista abreviado:J. Stat. Plann. Inference
ISSN:03783758
CODEN:JSPID
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03783758_v139_n10_p3570_Szretter

Referencias:

  • Cook, R.D., On the interpretation of regression plots (1994) Journal of the American Statistical Association, 89, pp. 177-189
  • Cook, R.D., Graphics for regressions with a binary response (1996) Journal of the American Statistical Association, 91, pp. 983-992
  • Cook, R.D., Principal Hessian directions revisited (1998) Journal of the American Statistical Association, 93, pp. 84-94
  • Cook, R.D., Fisher lecture: dimension reduction in regression (2007) Statistical Science, 22, pp. 1-26
  • Cook, R.D., Li, B., Dimension reduction for conditional mean in regression (2002) Annals of Statistics, 30, pp. 455-474
  • Cook, R.D., Ni, L., Sufficient dimension reduction via inverse regression: a minimum discrepancy approach (2005) Journal of the American Statistical Association, 100, pp. 410-428
  • Cook, R.D., Weisberg, S., Discussion of sliced inverse regression for dimension reduction, by K.C. Li (1991) Journal of the American Statistical Association, 86, pp. 328-332
  • Cook, R.D., Wetzel, N., Exploring regression structure with graphics (with discussion) (1993) Test, 2, pp. 33-100
  • Ferré, L., Determining the dimension in sliced inverse regression and related methods (1998) Journal of the American Statistical Association, 93, pp. 132-140
  • Li, K.C., Sliced inverse regression for dimension reduction (1991) Journal of the American Statistical Association, 86, pp. 316-327
  • Li, B., Zha, H., Chiaromonte, F., Contour regression: a general approach to dimension reduction (2005) The Annals of Statistics, 33, pp. 1580-1616
  • Li, K.-C., On principal Hessian directions for data visualization and dimension reduction: another application of Stein's lemma (1992) Journal of the American Statistical Association, 87, pp. 1025-1039
  • Schott, J.R., Determining the dimensionality in sliced inverse regression (1994) Journal of the American Statistical Association, 89, pp. 141-148
  • Seber, G.A.F., (1986) Multivariate Observations, , Wiley, New York
  • Zhu, Y., Zeng, P., Fourier methods for estimating the central subspace and the central mean subspace in regression (2006) Journal of the American Statistical Association, 101, pp. 1638-1651

Citas:

---------- APA ----------
Szretter, M.E. & Yohai, V.J. (2009) . The sliced inverse regression algorithm as a maximum likelihood procedure. Journal of Statistical Planning and Inference, 139(10), 3570-3578.
http://dx.doi.org/10.1016/j.jspi.2009.04.008
---------- CHICAGO ----------
Szretter, M.E., Yohai, V.J. "The sliced inverse regression algorithm as a maximum likelihood procedure" . Journal of Statistical Planning and Inference 139, no. 10 (2009) : 3570-3578.
http://dx.doi.org/10.1016/j.jspi.2009.04.008
---------- MLA ----------
Szretter, M.E., Yohai, V.J. "The sliced inverse regression algorithm as a maximum likelihood procedure" . Journal of Statistical Planning and Inference, vol. 139, no. 10, 2009, pp. 3570-3578.
http://dx.doi.org/10.1016/j.jspi.2009.04.008
---------- VANCOUVER ----------
Szretter, M.E., Yohai, V.J. The sliced inverse regression algorithm as a maximum likelihood procedure. J. Stat. Plann. Inference. 2009;139(10):3570-3578.
http://dx.doi.org/10.1016/j.jspi.2009.04.008