Dennis Cook:
Selected research articles
· On the role of partial least
squares in path analysis for the social sciences. Journal of Business Research. Available until 08/30/2023: https://authors.elsevier.com/c/1hOs9Xj-jftdp.
· Asymptotic distribution of one-component partial least
squares regression estimators in high dimensions. Canadian Journal of
Statistics, 2023.
· Partial least squares for simultaneous reduction of response
and predictor vectors in regression. Journal of Multivariate Analysis, 2023.
Shows how to use PLS to compress the response and predictor vectors
simultaneously in multivariate regression.
· A slice of multivariate
dimension reduction. Journal of Multivariate Analysis 2021. Invited
overview paper for JMVA's Jubilee Issue. Link active until 11/6/21.
· PLS regression algorithms
in the presence of nonlinearity. Chemometrics and Intelligent Laboratory
Systems 2021. Shows that PLS algorithms like NIPALS and SIMPLS are serviceable
in nonlinear regressions. Link active until 6/25/21.
· Envelopes:
A new chapter in partial least squares regression. Chemometrics, 2020.
Invited perspective paper.
· Partial
Least Squares Prediction in High-dimensional Regression. Annals of
Statistics, 2019. Shows that PLS can converge at a useful rate in
high-dimensional regressions.
· Principal
Components, Sufficient Dimension Reduction and Envelopes. Annual Review of
Statistics and its Applications, 2018.
· Big data and
partial least squares prediction, Canadian Journal of Statistics, 2018
· Scaled
predictor envelopes and partial least-squares regression, Technometrics,
2016. Gives a method to achieve scale invariance of the predictors in PLS.
· Simultaneous
Envelopes for Multivariate Linear Regression, Technometrics, 2015.
· Foundations
for Envelope Models and Methods, JASA, 2015.
· Envelopes
and reduced rank regression , Biometrika, 2015.
· Prediction in abundant
high-dimensional linear regression, EJS, 2013.
· Envelopes and partial least squares regression.
JRSS-B, 2013.
· Scaled
envelopes: scale-invariant and efficient estimation in multivariate linear
regression, Biometrika, 2013.
· Estimation of multivariate means with
heteroscedastic errors using envelope models, Statistica Sinica, 2013.
· Inner
envelopes: efficient estimation in multivariate linear regression,
Biometrika, 2012.
· Estimating sufficient reductions of the predictors in
abundant high-dimensional regressions. Supplement.
Errata. Annals of Statistics, 2012.
· Partial
envelopes for efficient estimation in multivariate linear regression,
Biometrika, 2011.
· Coordinate-independent sparse sufficient dimension
reduction and variable selection. Annals
of Statistics, 2010
· Envelope models for parsimonious and and efficient multivariate
regression (with discussion). Statistica Sinica, 2010 .
· Likelihood-based sufficient dimension reduction JASA, 2009,
197-208. Gives in part an ordering to directions in quadratic discriminant
analysis.
· Dimension reduction in regressions with exponential
family predictors, JCGS, 2009.
· Principal fitted components for dimension reduction in
regression, Statistical Science,
2009
· Covariance
reducing models: An alternative to spectral modelling of covariance matrices,
Biometrika, 2008.
· Successive direction extraction for estimating the central
subspace in a multi-index regression. J. Multivariate Analysis, 2008.
· Fisher Lecture: Dimension Reduction in Regression, Statistical Science, 2007, (with
discussion) Rejoinder
· Dimension
Reduction without Matrix Inversion, Biometrika, 2007. This gives neat
methodology for the n < p problem.
· Elevated Soil Lead, which appeared in the first issue
of The Annals of Applied Statistics.
· Optimal Sufficient Dimension
Reduction for the Conditional Mean in Multivariate Regression, Biometrika,
2007.
· Marginal Tests with SAVE, Biometrika, 2007.