Central subspaces review: methods and applications-Reference-Cited by-同舟云学术

Central subspaces review: methods and applications

Published:2022-01-01 Issue:none Volume:16 Page:
ISSN:1935-7516
Container-title:Statistics Surveys
language:
Short-container-title:Statist. Surv.

Author:

A. Rodrigues Sabrina¹,Huggins Richard²,Liquet Benoit³

Affiliation:

1. Department of Epidemiology and Biostatistics, School of Public Health Imperial College London, London UK

2. School of Mathematics and Statistics, The University of Melbourne, Australia

3. Laboratoire de Mathématiques et de leurs Applications de Pau, Université de Pau et des Pays de l’Adour, Pau, France [2mm] School of Mathematical and Physical Sciences, Macquarie University, Sydney, New South Wales, Australia

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference91 articles.

1. COOK, R. D. and WEISBERG, S. (1991). Sliced inverse regression for dimension reduction: Comment. Journal of the American Statistical Association 86 328–332.

2. FUKUMIZU, K. and LENG, C. (2014). Gradient-Based Kernel Dimension Reduction for Regression. Journal of the American Statistical Association 109 359–370.

3. ZHU, Y. and ZENG, P. (2006). Fourier methods for estimating the central subspace and the central mean subspace in regression. Journal of the American Statistical Association 101 1638–1651.

4. ADRAGNI, K. and RAIM, A. (2014). ldr: An R Software Package for Likelihood-Based Sufficient Dimension Reduction. Journal of Statistical Software, Articles 61 1–21.

5. ANAGNOSTOPOULOS, C. and HAND, D. J. (2012). hmeasure: The H-measure and other scalar classification performance metrics R package version 1.0.

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