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Matrix variate regressions and envelope models

Journal of the Royal Statistical Society. Series B, Statistical methodology, 2018-03, Vol.80 (2), p.387-408

2018

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Sufficient dimension reduction and prediction in regression

Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences, 2009-11, Vol.367 (1906), p.4385-4405

2009

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An Introduction to Envelopes: Dimension Reduction for Efficient Estimation in Multivariate Statistics

Cook, R. Dennis

1st edition., 2018

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Festschrift in Honor of R. Dennis Cook: Fifty Years of Contribution to Statistical Science

1st Edition 2021, 2021

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On the role of partial least squares in path analysis for the social sciences

Journal of business research, 2023-11, Vol.167, p.114132, Article 114132

2023

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Inner envelopes: efficient estimation in multivariate linear regression

Biometrika, 2012-09, Vol.99 (3), p.687-702

2012

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PARTIAL LEAST SQUARES PREDICTION IN HIGH-DIMENSIONAL REGRESSION

The Annals of statistics, 2019-04, Vol.47 (2), p.884-908

2019

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Sparse Minimum Discrepancy Approach to Sufficient Dimension Reduction with Simultaneous Variable Selection in Ultrahigh Dimension

Journal of the American Statistical Association, 2019-07, Vol.114 (527), p.1277-1290

2019

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Foundations for Envelope Models and Methods

Journal of the American Statistical Association, 2015-06, Vol.110 (510), p.599-611

2015

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Fused Estimators of the Central Subspace in Sufficient Dimension Reduction

Journal of the American Statistical Association, 2014-06, Vol.109 (506), p.815-827

2014

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Likelihood-Based Sufficient Dimension Reduction

Journal of the American Statistical Association, 2009-03, Vol.104 (485), p.197-208

2009

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Envelopes: A new chapter in partial least squares regression

Journal of chemometrics, 2020-10, Vol.34 (10), p.n/a

2020

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FAST ENVELOPE ALGORITHMS

Statistica Sinica, 2018-07, Vol.28 (3), p.1179-1197

2018

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Envelopes: A new chapter in partial least squares regression

Journal of chemometrics, 2020-10, Vol.34 (10), p.n/a

2020

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A convex formulation for high-dimensional sparse sliced inverse regression

Biometrika, 2018-12, Vol.105 (4), p.769-782

2018

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Simultaneous Envelopes for Multivariate Linear Regression

Technometrics, 2015-01, Vol.57 (1), p.11-25

2015

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Envelopes for multivariate linear regression with linearly constrained coefficients

Scandinavian journal of statistics, 2024-06, Vol.51 (2), p.429-446

2024

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Asymptotic distribution of one‐component partial least squares regression estimators in high dimensions

Canadian journal of statistics, 2024-03, Vol.52 (1), p.118-130

2024

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COORDINATE-INDEPENDENT SPARSE SUFFICIENT DIMENSION REDUCTION AND VARIABLE SELECTION

The Annals of statistics, 2010-12, Vol.38 (6), p.3696-3723

2010

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Big data and partial least-squares prediction

Canadian journal of statistics, 2018-03, Vol.46 (1), p.62-78

2018

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Algorithms for Envelope Estimation

Journal of computational and graphical statistics, 2016-01, Vol.25 (1), p.284-300

2016

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Envelope methods

Wiley interdisciplinary reviews. Computational statistics, 2020-03, Vol.12 (2), p.e1484-n/a

Cook, R. Dennis

2020

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Marginal tests with sliced average variance estimation

Biometrika, 2007-06, Vol.94 (2), p.285-296

2007

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LDR : A Package for Likelihood-Based Sufficient Dimension Reduction

Journal of statistical software, 2011-03, Vol.39 (3)

2011

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GrassmannOptim : An R Package for Grassmann Manifold Optimization

Journal of statistical software, 2012-07, Vol.50 (5)

2012

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Matrix variate regressions and envelope models

Sufficient dimension reduction and prediction in regression

An Introduction to Envelopes: Dimension Reduction for Efficient Estimation in Multivariate Statistics

Festschrift in Honor of R. Dennis Cook: Fifty Years of Contribution to Statistical Science

On the role of partial least squares in path analysis for the social sciences

Inner envelopes: efficient estimation in multivariate linear regression

PARTIAL LEAST SQUARES PREDICTION IN HIGH-DIMENSIONAL REGRESSION

Sparse Minimum Discrepancy Approach to Sufficient Dimension Reduction with Simultaneous Variable Selection in Ultrahigh Dimension

Foundations for Envelope Models and Methods

Fused Estimators of the Central Subspace in Sufficient Dimension Reduction

Likelihood-Based Sufficient Dimension Reduction

Envelopes: A new chapter in partial least squares regression

FAST ENVELOPE ALGORITHMS

Envelopes: A new chapter in partial least squares regression

A convex formulation for high-dimensional sparse sliced inverse regression

Simultaneous Envelopes for Multivariate Linear Regression

Envelopes for multivariate linear regression with linearly constrained coefficients

Asymptotic distribution of one‐component partial least squares regression estimators in high dimensions

COORDINATE-INDEPENDENT SPARSE SUFFICIENT DIMENSION REDUCTION AND VARIABLE SELECTION

Big data and partial least-squares prediction

Algorithms for Envelope Estimation

Envelope methods

Marginal tests with sliced average variance estimation

LDR : A Package for Likelihood-Based Sufficient Dimension Reduction

GrassmannOptim : An R Package for Grassmann Manifold Optimization

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