Browsing 01 Mathematical Sciences by Author "Wand, Matthew"

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Browsing 01 Mathematical Sciences by Author "Wand, Matthew"

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  • Gonzalez-Manteiga, W.; Sanchez-Sellero, C.; Wand, Matthew (Elsevier, 1996)
    Virtually all common bandwidth selection algorithms are based on a certain type of kernel functional estimator. Such estimators can be computationally very expensive, so in practice they are often replaced by fast binned ...
  • Ganguli, Bhaswati; Wand, Matthew (John Wiley & Sons Ltd., 2006)
    Asthma researchers have found some evidence that geographical variations in susceptibility to asthma could reflect the effect of community level factors such as exposure to violence. Our methodology was motivated by a study ...
  • Ganguli, Bhaswati; Staudenmayer, John; Wand, Matthew (Blackwell Publishing Ltd, 2005)
    This paper develops a likelihood-based method for fitting additive models in the presence of measurement error. It formulates the additive model using the linear mixed model representation of penalized splines. In the ...
  • Hall, Peter; Pham, Tung; Wand, Matthew; Wang, S. (Institute of Mathematical Statistics, 2011)
    We derive the precise asymptotic distributional behavior of Gaussian variational approximate estimators of the parameters in a single-predictor Poisson mixed model. These results are the deepest yet obtained concerning the ...
  • Samworth, R; Wand, Matthew (Institute of Mathematical Statistics, 2010)
    We study kernel estimation of highest-density regions (HDR). Our main contributions are two-fold. First, we derive a uniform-in-bandwidth asymptotic approximation to a risk that is appropriate for HDR estimation. This ...
  • Chacon, Jose; Duong, Tarn; Wand, Matthew (Academia Sinica, 2011)
    We investigate kernel estimators of multivariate density derivative functions using general (or unconstrained) bandwidth matrix selectors. These density derivative estimators have been relatively less well researched than ...
  • Crainiceanu, Ciprian; Ruppert, David; Wand, Matthew (American Statistical Association, 2005)
    Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for ...
  • Salganik, Mkhail; Milford, Edgar; Hardie, D.; Shaw, S.; Wand, Matthew (Wiley - VCH Verlag GmbH & Co. KGaA, 2005)
    Classifying monoclonal antibodies, based on the similarity of their binding to the proteins (antigens) on the surface of blood cells, is essential for progress in immunology, hematology and clinical medicine. The collaborative ...
  • Wand, Matthew; Ormerod, John (John Wiley & Sons Ltd, 2012)
    The aged number theoretic concept of continued fractions can enhance certain Bayesian computations. The crux of this claim is due to continued fraction representations of numerically challenging special function ratios ...
  • Crainiceanu, Ciprian; Ruppert, David; Claeskens, Gerda; Wand, Matthew (Oxford University Press, 2005)
    Penalised-spline-based additive models allow a simple mixed model representation where the variance components control departures from linear models. The smoothing parameter is the ratio of the random-coefficient and error ...
  • Ormerod, John; Wand, Matthew (American Statistical Association, 2010)
    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 ...
  • Pearce, N.; Wand, Matthew (Institute of Mathematical Statistics, 2009)
    Two areas of research ? longitudinal data analysis and kernel machines ? have large, but mostly distinct, literatures. This article shows explicitly that both fields have much in common with each other. In particular, many ...
  • Turlach, B.; Wand, Matthew (American Statistical Association, 1996)
    We investigate the extension of binning methodology to fast computation of several auxiliary quantities that arise in local polynomial smoothing. Examples include degrees of freedom measures, cross-validation functions, ...
  • Ganguli, Bhaswati; Wand, Matthew (Springer New York LLC, 2007)
    This paper develops inference for the significance of features such as peaks and valleys observed in additive modeling through an extension of the SiZer-type methodology of Chaudhuri and Marron (1999) and Godtliebsen et ...
  • Wand, Matthew (Academic Press, 2007)
    The Fisher information for the canonical link exponential family generalised linear mixed model is derived. The contribution from the fixed effects parameters is shown to have a particularly simple form.
  • Goldsmith, J; Wand, Matthew; Crainiceanu, C. (Institute of Mathematical Statistics, 2011)
    We introduce variational Bayes methods for fast approximate inference in functional regression analysis. Both the standard cross-sectional and the increasingly common longitudinal settings are treated. The methodology ...
  • Ormerod, John; Wand, Matthew (Amer Statistical Assoc, 2012)
    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 ...
  • Zhao, Yihao; Staudenmayer, John; Coull, B.; Wand, Matthew (The Institute of Mathematical Statistics, 2006)
    Linear mixed models are able to handle an extraordinary range of complications in regression-type analyses. Their most common use is to account for within-subject correlation in longitudinal data analysis. They are also ...
  • Nevillea, Sarah; Wand, Matthew (Elsevier, 2011)
    We devise a variationalBayes algorithm for fast approximate inference in Bayesian GeneralizedExtremeValue additive modelanalysis. Such models are useful for flexibly assessing the impact of continuous predictor variables ...
  • Fan, Y.; Leslie, David; Wand, Matthew (Institute of Mathematical Statistics, 2008)
    We present a sequential Monte Carlo sampler algorithm for the Bayesian analysis of generalised linear mixed models (GLMMs). These models support a variety of interesting regression-type analyses, but performing inference ...