·  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.