M. P. Wand

Publications (8)3.15 Total impact

• Source

  Available from: citeseerx.ist.psu.edu

  Article: Parsimonious Classification Via Generalized Linear Mixed Models

  G. Kauermann, J. T. Ormerod, M. P. Wand
  [show abstract] [hide abstract]
  ABSTRACT: We devise a classification algorithm based on generalized linear mixed model (GLMM) technology. The algorithm incorporates spline smoothing, additive model-type structures and model selection. For reasons of speed we employ the Laplace approximation, rather than Monte Carlo methods. Tests on real and simulated data show the algorithm to have good classification performance. Moreover, the resulting classifiers are generally interpretable and parsimonious. KeywordsAkaike Information Criterion-Feature selection-Generalized additive models-Penalized splines-Supervised learning-Model selection-Rao statistics-Variance components
  Journal of Classification 04/2012; 27(1):89-110. · 0.72 Impact Factor
• Source

  Available from: ArXiv

  Article: ON SEMIPARAMETRIC REGRESSION WITH O'SULLIVAN PENALIZED SPLINES

  M. P. Wand, J. T. Ormerod
  [show abstract] [hide abstract]
  ABSTRACT: An exposition on the use of O'Sullivan penalized splines in contemporary semiparametric regression, including mixed model and Bayesian formulations, is presented. O'Sullivan penalized splines are similar to P-splines, but have the advantage of being a direct generalization of smoothing splines. Exact expressions for the O'Sullivan penalty matrix are obtained. Comparisons between the two types of splines reveal that O'Sullivan penalized splines more closely mimic the natural boundary behaviour of smoothing splines. Implementation in modern computing environments such as Matlab, r and bugs is discussed.
  Australian &amp New Zealand Journal of Statistics 05/2008; 50(2):179 - 198. · 0.44 Impact Factor
• Article: On nonparametric discrimination using density differences

  P Hall, M P Wand
  Biometrika. 75:541-547.
• Source

  Available from: psu.edu

  Article: Parsimonious classification via generalised linear mixed models

  G. Kauermann, J. T. Ormerod, M. P. Wand
  [show abstract] [hide abstract]
  ABSTRACT: We devise a classification algorithm based on generalised linear mixed model (GLMM) technology. The algorithm incorporates spline smoothing, additive model-type structures and model selection. For reasons of speed we employ the Laplace approximation, rather than Monte Carlo methods. Tests on real and simulated data show the algorithm to have good classification performance. Moreover, the resulting classifiers are generally interpretable and parsimonious.
  Centre for Statistical & Survey Methodology Working Paper Series.
• Source

  Available from: uow.edu.au

  Article: Theory of Gaussian Variational Approximation for a Poisson Mixed Model

  P. Hall, J. T. Ormerod, M. P. Wand
  [show abstract] [hide abstract]
  ABSTRACT: Likelihood-based inference for the parameters of generalized linear mixed models is hindered by the presence of intractable integrals. Gaussian variational approximation provides a fast and effective means of approximate inference. We provide some theory for this type of approximation for a simple Poisson mixed model. In particular, we establish consistency at rate m−1/2 + n−1, where m is the number of groups and n is the number of repeated measurements.
  Centre for Statistical & Survey Methodology Working Paper Series.
• Source

  Available from: uow.edu.au

  Article: Variational Bayesian Inference for Parametric and Nonparametric Regression With Missing Data

  C. Faes, J. T. Ormerod, M. P. Wand
  Journal of the American Statistical Association 106(495):959-971. · 1.99 Impact Factor
• Source

  Available from: uow.edu.au

  Article: Gaussian Variational Approximate Inference for Generalized Linear Mixed Models

  J. T. Ormerod, M. P. Wand
  [show abstract] [hide abstract]
  ABSTRACT: Variational approximation methods have become a mainstay of contemporary Machine Learning methodology, but currently have little presence in Statistics. We devise an effective variational approximation strategy for fitting generalized linear mixed models (GLMM) appropriate for grouped data. It involves Gaussian approximation to the distributions of random effects vectors, conditional on the responses. We show that Gaussian variational approximation is a relatively simple and natural alternative to Laplace approximation for fast, non-Monte Carlo, GLMM analysis. Numerical studies show Gaussian variational approximation to be very accurate in grouped data GLMM contexts. Finally, we point to some recent theory on consistency of Gaussian variational approximation in this context.
  Centre for Statistical & Survey Methodology Working Paper Series.
• Source

  Available from: uow.edu.au

  Article: Explaining Variational Approximations

  J. T. Ormerod, M. P. Wand
  [show abstract] [hide abstract]
  ABSTRACT: Variational approximations facilitate approximate inference for the parameters in complex statistical models and provide fast, deterministic alternatives to Monte Carlo methods. However, much of the contemporary literature on variational approximations is in Computer Science rather than Statistics, and uses terminology, notation and examples from the former field. In this article we explain variational approximation in statistical terms. In particular, we illustrate the ideas of variational approximation using examples that are familiar to statisticians.
  Centre for Statistical & Survey Methodology Working Paper Series.