Under consideration for publication

Jiang, J., Wand, M.P. and Ghosh, S.
Precise Asymptotics for Linear Mixed Models with Crossed Random Effects.
[PDF file]

He, V.X. and Wand, M.P.
The Grouped Horseshoe Distribution and Its Statistical Properties.
[PDF file] [PDF file of article's web-supplement]

Published in 2025 or soon after

Maestrini, L., Aykroyd, R.G. and Wand, M.P.
A Variational Inference Framework for Inverse Problems.
[PDF file] [PDF file of article's web-supplement]
Computational Statistics and Data Analysis, (2025), 202, 108055, 1-15.

Menictas, M., Oates, C.J. and Wand, M.P.
Online Semiparametric Regression via Sequential Monte Carlo.
[PDF file] [Accompanying movies]
Australian and New Zealand Journal of Statistics, full publication details pending.

Published in 2024

Maestrini, L., Bhaskaran, A. and Wand, M.P.
Second Term Improvement to Generalized Linear Mixed Model Asymptotics.
Biometrika, (2024), 111, 1077-1084.
[PDF file] [PDF file of article's web-supplement] [ZIP archive file containing computer code]

He, V.X. and Wand, M.P.
Bayesian Generalized Additive Model Selection Including a Fast Variational Option.
Advances in Statistical Analysis, (2024), 108, 639-668.
[PDF file] [PDF file of article's web-supplement] [Accompanying R package]

Published in 2023

Menictas, M., Di Credico, G. and Wand, M.P.
Streamlined Variational Inference for Linear Mixed Models with Crossed Random Effects.
Journal of Computational and Graphical Statistics, (2023), 32, 99-115.
[PDF file] [PDF file of article's web-supplement]

Hughes, D.M., García-Finaña, M. and Wand, M.P.
Fast Approximate Inference for Multivariate Longitudinal Data.
Biostatistics, (2023), 24, 177-192.
[PDF file] [PDF file of article's web-supplement]

Bhaskaran, A. and Wand, M.P.
Dispersion Parameter Extension of Precise Generalized Linear Mixed Model Asymptotics.
Statistics and Probability Letters, (2023), 193, Article 109691.
[PDF file] [PDF file of article's web-supplement]

Published in 2022

Degani, E., Maestrini, L., Toczydłowska, D. and Wand, M.P.
Sparse Linear Mixed Model Selection via Streamlined Variational Bayes.
Electronic Journal of Statistics, (2022), 16, 5182-5225.
[PDF file] [PDF file of article's web-supplement]

Jiang, J., Wand, M.P. and Bhaskaran, A.
Usable and Precise Asymptotics for Generalized Linear Mixed Model Analysis and Design.
Journal of the Royal Statistical Society, Series B, (2022), 84, 55-82.
[PDF file] [PDF file of an erratum notice] [ZIP archive file containing computer code]

Wand, M.P. and J.C.F. Yu
Density Estimation via Bayesian Inference Engines.
Advances in Statistical Analysis, (2022), 106, 199-216.
[PDF file] [Accompanying R package]

Published in 2021

Menictas, M., Nolan, T.H., Simpson, D.G. and Wand, M.P.
Streamlined Variational Inference for Higher Level Group-Specific Curve Models.
Statistical Modelling, (2021), 21, 479-519.
[PDF file] [ZIP archive file containing computer code and data]

Maestrini, L. and Wand, M.P.
The Inverse G-Wishart Distribution and Variational Message Passing.
Australian and New Zealand Journal of Statistics, (2021), 63, 517-541.
[PDF file] [PDF file of article's web supplement] [ZIP archive file containing computer code]

Published in 2020

Nolan, T.H., Menictas, M. and Wand, M.P.
Streamlined Computing for Variational Inference with Higher Level Random Effects.
Journal of Machine Learning Research, (2020), 21 (157), 1-62.
[PDF file] [ZIP archive file containing computer code]

Hall, P., Johnstone, I.M., Ormerod, J.T., Wand, M.P. and Yu, J.C.F.
Fast and Accurate Binary Response Mixed Model Analysis via Expectation Propagation.
Journal of the American Statistical Association, (2020), 115, 1902-1916.
[PDF file] [PDF file of article's web-supplement] [Accompanying R package]

Nolan, T.H. and Wand, M.P.
Streamlined Solutions to Multilevel Sparse Matrix Problems.
ANZIAM Journal, (2020), 62, 18-41.
[PDF file]

Chen, W.Y. and Wand, M.P.
Factor Graph Fragmentization of Expectation Propagation.
Journal of the Korean Statistical Society, (2020), 49, 722-756.
[PDF file] [PDF file of article's web-supplement] [ Article on journal web-site]

Published in 2019

McLean, M.W. and Wand, M.P.
Variational Message Passing for Elaborate Response Regression Models.
Bayesian Analysis, (2019), 14, 371-398.
[PDF file] [PDF file of article's web-supplement]

Published in 2018

Pham, T.H. and Wand, M.P.
Generalized Additive Mixed Model Analysis via gammSlice.
Australian and New Zealand Journal of Statistics, (2018), 60, 279-300.
[PDF file] [Accompanying R package]

Maestrini, L. and Wand, M.P.
Variational Message Passing for Skew t Regression.
Stat, (2018), e196, 1-11.
[PDF file] [PDF file of article's web-supplement]

Kim, A.S.I. and Wand, M.P.
On Expectation Propagation for Generalised, Linear and Mixed Models.
Australian and New Zealand Journal of Statistics, (2018), 60, 75-102.
[PDF file]

Liu, S.H., Bobb, J.F., Henn, B.C., Schnaas, L., Tellez-Rojo, M.M., Gennings, C.,
Arora, M., Wright, R.O., Coull, B.A. and Wand, M.P.
Modeling the Health Effects of Time-Varying Complex Environmental Mixtures: Mean Field
Variational Bayes for Lagged Kernel Machine Regression.
Environmnetrics, (2018), 29, 75-102.
[PDF file]

Luts, J., Wang, S.S.J., Ormerod, J.T. and Wand, M.P.
Semiparametric Regression Analysis via Infer.NET.
Journal of Statisical Software, (2018), 87, Issue 2, 1-37.
[PDF file]

Published in 2017

Wand, M.P.
Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models
via Message Passing (with discussion).
Journal of the American Statistical Association, (2017), 112, 137-168.
[PDF file of article and discussion] [PDF file of article's web-supplement]

Nolan, T.H. and Wand, M.P.
Accurate Logistic Variational Message Passing: Algebraic and Numerical Details.
Stat, (2017), 6, 102-112.
[PDF file of main article] [PDF file of web-supplement]

Published in 2016

Ryan, L.M., Wand, M.P. and Malecki, A.A.
Bringing Coals to Newcastle.
Significance, (2016), 13, 32-37.
[PDF file]

Rohde, D. and Wand, M.P.
Semiparametric Mean Field Variational Bayes: General Principles and Numerical Issues.
Journal of Machine Learning Research, (2016), 17 (172), 1-47.
[PDF file]

Delaigle, A. and Wand, M.P.
A Conversation with Peter Hall.
Statistical Science, (2016), 31, 275-304.
[PDF file]

Dubossarsky, E., Friedman, J.H., Ormerod, J.T. and Wand, M.P.
Wavelet-based Gradient Boosting.
Statistics and Computing, (2016), 26, 93-105.
[PDF file]

Lee, C.Y.Y. and Wand, M.P.
Variational Methods for Fitting Complex Bayesian Mixed Effects Models to Health Data.
Statistics in Medicine, (2016), 35, 165-188.
[PDF file]

Kim, A.S.I. and Wand, M.P.
The Explicit Form of Expectation Propagation for a Simple Statistical Model.
Electronic Journal of Statistics, (2016), 10, 550-581.
[PDF file] [ZIP archive file containing computer code]

Lee, C.Y.Y. and Wand, M.P.
Streamlined Mean Field Variational Bayes for Longitudinal and Multilevel Data Analysis.
Biometrical Journal, (2016), 58, 868-895.
[PDF file]

Published in 2015

Menictas, M. and Wand, M.P.
Variational Inference for Heteroscedastic Semiparametric Regression.
Australian and New Zealand Journal of Statistics, (2015), 57, 119-138.
[PDF file]

Luts, J. and Wand, M.P.
Variational Inference for Count Response Semiparametric Regression.
Bayesian Analysis, (2015), 10, 991-1023.
[PDF file]

Published in 2014

Luts, J., Broderick, T. and Wand, M.P.
Real-time Semiparametric Regression.
Journal of Computational and Graphical Statistics, (2014), 23, 589-615.
[PDF file]

Neville, S.E., Ormerod, J.T. and Wand, M.P.
Mean Field Variational Bayes for Continuous Sparse Signal Shrinkage: Pitfalls and Remedies.
Electronic Journal of Statistics, (2014), 8, 1113-1151.
[PDF file]

Wand, M.P.
Fully Simplified Multivariate Normal Updates in Non-Conjugate Variational Message Passing.
Journal of Machine Learning Research, (2014), 15, 1351-1369.
[PDF file]

Published in 2013

Pham, T., Ormerod, J.T. and Wand, M.P.
Mean Field Variational Bayesian Inference for Nonparametric Regression with Measurement Error.
Computational Statistics and Data Analysis, (2013), 68, 375-387.
[PDF file]

Huang, A. and Wand, M.P.
Simple Marginally Noninformative Prior Distributions for Covariance Matrices.
Bayesian Analysis, (2013), 2, Number 2, 439-452.
[PDF file]

Menictas, M. and Wand, M.P.
Variational Inference for Marginal Longitudinal Semiparametric Regression.
Stat, (2013), 2, 61-71.
[PDF file]

Published in 2012

Wand, M.P. and Ormerod, J.T.
Continued Fraction Enhancement of Bayesian Computing.
Stat, (2012), 1, 31-41.
[PDF file]

Ormerod, J.T. and Wand, M.P.
Gaussian Variational Approximate Inference for Generalized Linear Mixed Models.
Journal of Computational and Graphical Statistics, (2012), 21, 2-17.
[PDF file of main article] [PDF file of web-supplement]

Published in 2011

Hall, P., Pham, T., Wand, M.P. and Wang, S.S.J.
Asymptotic Normality and Valid Inference for Gaussian Variational Approximation.
The Annals of Statistics, (2011), 39, 2502-2532.
[PDF file]

Faes, C., Ormerod, J.T. and Wand, M.P.
Variational Bayesian Inference for Parametric and Nonparametric Regression with Missing Data.
Journal of the American Statistical Association, (2011), 106, 959-971.
[PDF file of main article] [PDF file of web-supplement]

Neville, S.E., Palmer, M.J. and Wand, M.P.
Generalized Extreme Value Additive Model Analysis via Mean Field Variational Bayes.
Australian and New Zealand Journal of Statistics, (2011), 53, 305-330.
[PDF file]

Hall, P., Ormerod, J.T. and Wand, M.P.
Theory of Gaussian Variational Approximation for a Generalised Linear Mixed Model.
Statistica Sinica, (2011), 21, 269-389.
[PDF file]

Chacon, J.E., Duong, T. and Wand, M.P.
Asymptotics for General Multivariate Kernel Density Derivative Estimators.
Statistica Sinica, (2011), 21, 807-840.
[PDF file]

Wand, M.P., Ormerod, J.T., Padoan, S.A. and Fruhwirth, R.
Mean Field Variational Bayes for Elaborate Distributions.
Bayesian Analysis, (2011), 6, 847-900.
[PDF file]

Wang, S.S.J and Wand, M.P.
Using Infer.NET for Statistical Analyses.
The American Statistician, (2011), 65, 115-126.
[PDF file]

Wand, M.P. and Ormerod, J.T.
Penalized Wavelets: Embedding Wavelets into Semiparametric Regression.
Electronic Journal of Statistics, (2011), 5, 1654-1717.
[PDF file (article)] [ZIP archive file containing computer code]

Goldsmith, J., Wand, M.P. and Crainiceanu, C.
Functional Regression via Variational Bayes.
Electronic Journal of Statistics, (2011), 5, 572-602.
[PDF file]

Published in 2010

Marley, J.K. and Wand, M.P.
Non-Standard Semiparametric Regression via BRugs.
Journal of Statistical Software, (2010), Volume 37, Issue 5, 1-30.
[ PDF file; Code and Data Files]

Al Kadiri, M., Carroll, R.J. and Wand, M.P.
Marginal Longitudinal Semiparametric Regression via Penalized Splines.
Statistics and Probability Letters, (2010), 80, 1242-1252.
[PDF file]

Ormerod, J.T. and Wand, M.P.
Explaining Variational Approximations.
The American Statistician, (2010), 64, 140-153.
[PDF file]

Samworth, R.J. and Wand, M.P.
Asymptotics and Optimal Bandwidth Selection for Highest Density Region Estimation.
The Annals of Statistics, (2010), 38, 1767-1792.
[PDF file] [Accompanying R package]

Naumann, U., Luta, G. and Wand, M.P.
The curvHDR Method for Gating Flow Cytometry Samples.
BMC Bioinformatics, (2010), 11:44, 1-13.
[PDF file] [Accompanying R package]

Kauermann, G., Ormerod, J.T. and Wand, M.P.
Parsimonious Classification via Generalised Linear Mixed Models.
Journal of Classification, (2010), 27, 89-110.
[PDF file]

Published in 2009

Ruppert, D., Wand, M.P. and Carroll, R.J.
Semiparametric Regression During 2003-2007.
Electronic Journal of Statistics, (2009), 3, 1193-1256.
[PDF file]

Staudenmayer, J., Lake, E.E. and Wand, M.P.
Robustness for General Design Mixed Models Using the t-Distribution.
Statistical Modelling, (2009), 9, 235-255.
[PDF file]

Naumann, U. and Wand, M.P.
Automation in High-Content Flow Cytometry Screening.
Cytometry Part A, (2009), 75A, 789-797.
[PDF file]

Pearce, N.D. and Wand, M.P.
Explicit Connections between Longitudinal Data Analysis and Kernel Machines.
Electronic Journal of Statistics, (2009), 3, 797-823.
[PDF file]

Duong, T., Koch, I. and Wand, M.P.
Highest Density Difference Region Estimation with Application to Flow Cytometric Data.
Biometrical Journal, (2009), 51, 504-521.
[PDF file]

Wand, M.P.
Semiparametric Regression and Graphical Models.
[PDF file]
This article was published in Australian and New Zealand
Journal of Statistics, 2009; 51: 9 to 41, available online
at Blackwell Synergy (www.blackwell-synergy.com).

Ormerod, J.T. and Wand, M.P.
Comment on Paper by Rue, Martino and Chopin.
Journal of the Royal Statistical Society, Series B, 71, 377-378.
[PDF file]

Published in 2008

Fan, Y., Leslie, D.S. and Wand, M.P.
Generalised Linear Mixed Model Analysis via Sequential Monte Carlo Sampling.
Electronic Journal of Statistics, (2008), 2, 916-938.
[ PDF file]

Duong, T., Cowling, A., Koch, I. and Wand, M.P.
Feature Significance for Multivariate Kernel Density Estimation.
Computational Statistics and Data Analysis, (2008), 52, 4225-4242
[PDF file]

Kuo, F., Dunsmuir, W.T.M., Sloan, I.H., Wand, M.P. and Womersley, R.S.
Quasi-Monte Carlo for Highly Structured Generalised Response Models.
Methodology and Computing in Applied Probability, (2008), 10, 239-275.
[PDF file]

Smith, A.D.A.C. and Wand, M.P.
Streamlined Variance Calculations for Semiparametric Mixed Models.
Statistics in Medicine, (2008), 27, 435-448.
[ PDF file] [Appendix code]

Wand, M.P. and Ormerod, J.T.
On Semiparametric Regression with O'Sullivan Penalised Splines.
This article was published in Australian and New Zealand
Journal of Statistics, 2008; 50: 179 to 198, available online
at Blackwell Synergy (www.blackwell-synergy.com).
[PDF file] [Correction notice (PDF file)] [Appendix code]

Ormerod, J.T., Wand, M.P. and Koch, I.
Penalised Spline Support Vector Classifiers: Computational Issues.
Computational Statistics, (2008), 23, 623-641.
[PDF file] [Accompanying R package]

Padoan, S.A. and Wand, M.P.
Mixed Model-based Additive Models for Sample Extremes.
Statistics and Probability Letters, (2008), 78, 2850-2858.
[PDF file]

Published in 2007

Wand, M.P.
Fisher Information for Generalised Linear Mixed Models.
Journal of Multivariate Analysis, (2007), 98, 1412-1416.
[PDF file]

Ganguli, B. and Wand, M.P.
Feature significance in generalized additive models.
Statistics and Computing, (2007). 17, 179-192.
[ PDF file]

Published in 2006

Wand, M.P.
Support Vector Machine Classification.
Parabola, (2006), 42 (2), 21-37.
[PDF file] [ co lo ur version PDF file]

Pearce, N.D. and Wand, M.P.
Penalised Splines and Reproducing Kernel Methods.
The American Statistician, (2006), 60, 233-240.
[ PDF file]

Zhao, Y., Staudenmayer, J., Coull, B.A. and Wand, M.P.
General Design Bayesian Generalized Linear Mixed Models.
Statistical Science, (2006), 21, 35-51.
[PDF file]

Ganguli, B. and Wand, M.P.
Additive Models for Geo-Referenced Failure Time Data.
Statistics in Medicine, 2004, 25, 2469-2482.
[PDF file]

Published in 2005

Crainiceanu, C., Ruppert, D. and Wand, M.P.
Bayesian Analysis for Penalized Spline Regression Using WinBUGS.
Volume 14, 2005, Issue 14 of Journal of Statistical Software, 1-24.
[PDF file]

Ganguli, B., Staudenmayer, J. and Wand, M.P.
Additive Models with Predictors Subject to Measurement Error.
[PDF file]
This article was published in Australian and New Zealand
Journal of Statistics, 2005; 47: 193 to 202, available online
at Blackwell Synergy (www.blackwell-synergy.com).

Durban, M., Harezlak, J., Wand, M.P. and Carroll, R.J.
Simple Fitting of Subject-specific Curves for Longitudinal Data.
Statistics in Medicine, (2005), 24, 1153-1167.
[PDF file]

Crainiceanu, C., Ruppert, D., Claeskens, G. and Wand, M.P.
Exact Likelihood Ratio Tests for Penalised Splines.
Biometrika, (2005), 92, 91-103.
[PDF file]

Salganik, M.P., Milford, E.L., Hardie, D.L., Shaw, S. and Wand, M.P.
Classifying Antibodies using Flow Cytometry Data: Class Prediction and Class Discovery.
Biometrical Journal, (2005), 47, 740-745.
[PDF file]

Ganguli, B. and Wand, M.P.
SemiPar 1.0 Users' Manual.
[PDF file]

Published in 2004

Salganik, M.P., Wand, M.P. and Lange, N.
Comparison of Feature Significance Quantile Approximations.
[PDF file]
This article was published in Australian and New Zealand
Journal of Statistics, 2004; 46: 569 to 581, available online
at Blackwell Synergy (www.blackwell-synergy.com).

Ganguli, B. and Wand, M.P.
Feature Significance in Geostatistics.
Journal of Computational and Graphical Statistics, 2004.
13, 954-973
[PDF file]

Ngo, L. and Wand, M.P.
Smoothing with Mixed Model Software.
Volume 9, 2004, Issue 1 of Journal of Statistical Software, 1-54.
[paper, code and data]

Published in 2003

Kammann, E.E. and Wand, M.P.
Geoadditive Models.
Journal of the Royal Statistical Society, Series C, 52, 1-18.
[PDF file]

Wand, M.P.
Smoothing and Mixed Models.
Computational Statistics, 18, 223-249.
[PDF file]

Published in 2002

Wand, M.P.
Vector Differential Calculus in Statistics.
The American Statistician, 56, 55-62.
[PDF file]

Aerts, M., Claeskens, G. and Wand, M.P.
Some Theory for Penalized Spline Generalized Additive Models.
Journal of Statistical Planning and Inference, 103, 455-470.
[PDF file]

Cai, T., Hyndman, R.J. and Wand, M.P.
Mixed Model-Based Hazard Estimation.
Journal of Computational and Graphical Statistics, 11, 784-798.
[PDF file]

Betensky, R.A., Lindsey, J.C., Ryan, L.M. and Wand, M.P.
A Local Likelihood Proportional Hazards Model for Interval Censored Data.
Statistics in Medicine, 21, 263-275.
[PDF file]

Published in 2001

Mammen, E., Marron, J.S., Turlach, B.A. and Wand, M.P.
A General Projection Framework for Constained Smoothing.
Statistical Science, 16, 232-248.
[PDF file]

Coull, B.A., Schwartz, J. and Wand, M.P.
Respiratory Health and Air Pollution: Additive Mixed Model Analyses.
Biostatistics, 2, 337-349.
[PDF file]

Coull, B.A., Ruppert, D. and Wand, M.P.
Simple Incorporation of Interactions into Additive Models.
Biometrics, 57, 539-545.
[PDF file]

Parise, H., Wand, M.P., Ruppert, D. and Ryan L.
Incorporation of Historical Controls Using Semiparametric Mixed Models.
Journal of the Royal Statistical Society, Series C, 50, 31-42.
[PDF file]

French, J.L., Kammann, E.E. and Wand, M.P.
Comment on Paper by Ke and Wang.
Journal of the American Statistical Association, 96, 1285-1288.
[PDF file]

Published in 2000

Wand, M.P.
A Comparison of Regression Spline Smoothing Procedures.
Computational Statistics, 15, 443-462.
[PDF file]

Thurston, S.W., Wand, M.P. and Wiencke, J.K.
Negative Binomial Additive Models.
Biometrics, 56, 139-144.
[PDF file]

Zanobetti, A., Wand, M.P., Schwartz, J. and Ryan, L.M.
Generalized Additive Distributed Lag Models: Quantifying Mortality Displacement.
Biostatistics, 1, 279-292.
[PDF file]

Published in 1999

Wand, M.P.
A Central Limit Theorem for Local Polynomial Backfitting Estimators.
Journal of Multivariate Analysis, 70, 57-65.
[PDF file]

Wand, M.P.
On the Optimal Amount of Smoothing in Penalized Spline Regression.
Biometrika, 86, 936-940.
[PDF file]

Opsomer, J.D., Ruppert, D., Wand, M.P., Holst, U. and Hössjer, O.
Kriging with Nonparametric Variance Function Estimation.
Biometrics, 55, 704-710.
[PDF file]

Gijbels, I., Pope, A. and Wand, M.P.
Understanding Exponential Smoothing via Kernel Regression.
Journal of the Royal Statistical Society, Series B, 61, 39-50.
[PDF file]

Betensky, R.A., Lindsey, J.C., Ryan, L.M. and Wand, M.P.
Local EM Estimation of the Hazard Function for Interval Censored Data.
Biometrics, 55, 238-245.
[PDF file]

Brumback, A., Ruppert, D. and Wand, M.P.
Comment on Paper by Shively, Kohn and Wood.
Journal of the American Statistical Association, 94, 794-797.
[PDF file]

Published in 1998

Wand, M.P.
Finite Sample Performance of Deconvolving Density Estimators.
Statistics and Probability Letters, 37, 131-139.
[PDF file]

Augustyns, I. and Wand, M.P.
Bandwidth Selection for Local Polynomial Smoothing of Multinomial Data.
Computational Statistics, 13, 447-461.
[PDF file]

Published in 1997

Wand, M.P.
Data-based Choice of Histogram Bin Width.
The American Statistician, 51, 59-64.
[PDF file]

Hyndman, R.J. and Wand, M.P.
Nonparametric Autocovariance Function Estimation.
Australian Journal of Statistics, 39, 337-354.
[PDF file]

Ruppert, D., Wand, M.P., Holst, U. and Hössjer, O.
Local Polynomial Variance Function Estimation.
Technometrics, 39, 262-273.
[PDF file]

Carroll, R.J., Fan, J., Gijbels, I. and Wand, M.P.
Generalized Partially Linear Single-Index Models.
Journal of the American Statistical Association, 92, 477-489.
[PDF file]

Wand, M.P. and Gutierrez, R.G.
Exact Risk Approaches to Smoothing Parameter Selection.
Journal of Nonparametric Statistics, 8, 337-354.
[PDF file]

Published in 1996

Hall, P. and Wand, M.P.
On the Accuracy of Binned Kernel Density Estimators.
Journal of Multivariate Analysis, 56, 165-184.
[PDF file]

Turlach, B.A. and Wand, M.P.
Fast Computation of Auxiliary Quantities in Local Polynomial Regression.
Journal of Computational and Graphical Statistics, 5, 337-350.
[PDF file]

González-Manteiga, W., Sánchez-Sellero, C. and Wand, M.P.
Accuracy of Binned Kernel Functional Approximations.
Computational Statistics and Data Analysis, 22, 1-16.
[PDF file]

Published in 1995

Ruppert, D., Sheather, S.J. and Wand, M.P.
An Effective Bandwidth Selector for Local Least Squares Regression.
Journal of the American Statistical Association, 90, 1257-1270.
[PDF file]

Fan, J., Heckman, N.E. and Wand, M.P.
Local Polynomial Kernel Regression for Generalized Linear Models and Quasi-Likelihood Functions.
Journal of the American Statistical Association, 90, 141-150.
[PDF file]

Herrmann, E., Engel, J., Wand, M.P. and Gasser, T.
A Bandwidth Selector for Bivariate Kernel Regression.
Journal of the Royal Statistical Society, Series B, 57, 171-180.
[PDF file]

Aldershof, B., Marron, J.S., Park, B.U. and Wand, M.P.
Facts About the Gaussian Probability Density Function.
Applicable Analysis, 59, 289-306.
[PDF file]

Published in 1994

Wand, M.P.
Fast Computation of Multivariate Kernel Estimators.
Journal of Computational and Graphical Statistics, 3, 433-445.
[PDF file]

Ruppert, D. and Wand, M.P.
Multivariate Locally Weighted Least Squares Regression.
The Annals of Statistics, 22, 1346-1370.
[PDF file]

Wand, M.P. and Jones, M.C.
Multivariate Plug-in Bandwidth Selection.
Computational Statistics, 9, 97-116.
[PDF file]

Published in 1993

Wand, M.P. and Jones, M.C.
Comparison of Smoothing Parameterizations in Bivariate Kernel Density Estimation.
Journal of the American Statistical Association, 88, 520-528.
[PDF file]

Devroye, L. and Wand, M.P.
On the Effect of Density Shape on the Performance of its Kernel Estimate.
Statistics, 24, 215-233.
[PDF file]

M.P. Wand and Devroye, L.
How Easy is a Given Density to Estimate?
Computational Statistics and Data Analysis, 16, 313-323.
[PDF file]

Published in 1992

Marron, J.S. and Wand, M.P.
Exact Mean Integrated Squared Error.
The Annals of Statistics, 20, 712-736.
[PDF file]

Wand, M.P.
Error Analysis for General Multivariate Kernel Estimators.
Journal of Nonparametric Statistics, 2, 1-15.
[PDF file]

Wand, M.P.
Finite Sample Performance of Density Estimators Under Moving Average Dependence.
Statistics and Probability Letters, 13, 109-115.
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Ruppert, D. and Wand, M.P.
Correcting for Kurtosis in Density Estimation.
Australian Journal of Statistics, 34, 19-29.
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Jones, M.C. and Wand, M.P.
Asymptotic Effectiveness of Some Higher Order Kernels.
Journal of Statistical Planning and Inference, 31, 15-21.
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Published in 1991

Wand, M.P., Marron, J.S. and Ruppert, D.
Transformations in Density Estimation (with discussion).
Journal of the American Statistical Association, 86, 343-361.
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Scott, D.W. and Wand, M.P.
Feasibility of Multivariate Density Estimates.
Biometrika, 78, 197-205.
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Carroll, R.J. and Wand, M.P.
Semiparametric Estimation in Logistic Measurement Error Models.
Journal of the Royal Statistical Society, Series B, 53, 573-585.
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Published in 1990

Wand, M.P.
On Exact L₁ Rates of Convergence in Nonparametric Kernel Regression.
Scandinavian Journal of Statistics, 18, 197-204.
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Wand, M.P. and Schucany, W.R.
Gaussian-based Kernels.
Canadian Journal of Statistics, 18, 197-204.
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Härdle, W., Marron, J.S. and Wand, M.P.
Bandwidth Choice for Density Derivatives.
Journal of the Royal Statistical Society, Series B, 52, 223-232.
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Published in 1988

Hall, P. and Wand, M.P.
Minimizing L₁Distance in Nonparametric Density Estimation.
Journal of Multivariate Analysis, 26, 59-88.
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Hall, P. and Wand, M.P.
Minimizing L₁On Nonparametric Discrimination using Density Differences.
Biometrika, 75, 541-547.
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Hall, P. and Wand, M.P.
Minimizing L₁On the Minimization of Absolute Distance in Kernel Density Estimation.
Statistics and Probability Letters, 6, 311-314.
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