Kernel Smoothing - Matt P. Wand; M. Chris Jones (Springer, 1995)

Molecular identification using flow cytometry histograms and information theory. - Qing T. Zeng; Alan J. Young; Aziz A. Boxwala; James Rawn; W. Long; Matthew Wand; Mikhail Salganik; Edgar L. Milford; Steven J. Mentzer; Robert A. Greenes (AMIA, 2001)

Using a neural network with flow cytometry histograms to recognize cell surface protein binding patterns. - Eun-Young Kim; Qing T. Zeng; James Rawn; Matthew Wand; Alan J. Young; Edgar L. Milford; Steven J. Mentzer; Robert A. Greenes (AMIA, 2002)

Matching of flow-cytometry histograms using information theory in feature space. - Qing T. Zeng; Matthew Wand; Alan J. Young; James Rawn; Edgar L. Milford; Steven J. Mentzer; Robert A. Greenes (AMIA, 2002)

Feature significance in generalized additive models. - B. Ganguli; Matt P. Wand (Stat. Comput., 2007)

Feature significance for multivariate kernel density estimation. - Tarn Duong; Arianna Cowling; Inge Koch; Matt P. Wand (Comput. Stat. Data Anal., 2008)

Penalised spline support vector classifiers: computational issues. - John T. Ormerod; Matthew P. Wand; Inge Koch (Comput. Stat., 2008)

Recent Bioinformatics Advances in the Analysis of High Throughput Flow Cytometry Data. - Raphael Gottardo; Ryan Remy Brinkman; George Luta; Matthew P. Wand (Adv. Bioinformatics, 2009)

The curvHDR method for gating flow cytometry samples. - Ulrike Naumann; George Luta; Matthew P. Wand (BMC Bioinform., 2010)

Parsimonious Classification Via Generalized Linear Mixed Models. - Göran Kauermann; John T. Ormerod; Matthew P. Wand (J. Classif., 2010)

Comment. - John T. Ormerod; Matthew P. Wand (Technometrics, 2012)

Mean field variational Bayesian inference for nonparametric regression with measurement error. - Tung H. Pham; John T. Ormerod; Matthew P. Wand (Comput. Stat. Data Anal., 2013)

Fully simplified multivariate normal updates in non-conjugate variational message passing. - Matt P. Wand (J. Mach. Learn. Res., 2014)

Semiparametric Mean Field Variational Bayes: General Principles and Numerical Issues. - David Rohde; Matt P. Wand (J. Mach. Learn. Res., 2016)

Wavelet-based gradient boosting. - Eugene Dubossarsky; Jerome H. Friedman; John T. Ormerod; Matthew P. Wand (Stat. Comput., 2016)

Rigorous statistical analysis of HTTPS reachability. - George Michaelson; Matthew Roughan; Jonathan Tuke; Matt P. Wand; Randy Bush (CoRR, 2017)

Rasch Analysis of HTTPS Reachability. - George Geoffrey Michaelson; Matthew Roughan; Jonathan Tuke; Matt P. Wand; Randy Bush (Networking, 2018)

Streamlined Variational Inference with Higher Level Random Effects. - Tui H. Nolan; Marianne Menictas; Matt P. Wand (J. Mach. Learn. Res., 2020)

Streamlined Variational Inference for Linear Mixed Models with Crossed Random Effects. - Marianne Menictas; Gioia Di Credico; Matt P. Wand (J. Comput. Graph. Stat., 2023)

A variational inference framework for inverse problems. - Luca Maestrini; Robert G. Aykroyd; Matt P. Wand (Comput. Stat. Data Anal., 2025)

Scalable Subset Selection in Linear Mixed Models. - Ryan Thompson; Matt P. Wand; Joanna J. J. Wang (CoRR, 2025)