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.
Least Squares Prediction in High-dimensional Regression. Annals of
Statistics, 2019. Shows that PLS can converge at a useful rate in
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.
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
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,
Principal fitted components for
dimension reduction in regression, Statistical
Covariance reducing models: An
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
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,