• 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.