Browsing 01 Mathematical Sciences by Author "Wand Matt"

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Browsing 01 Mathematical Sciences by Author "Wand Matt"

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  • Additive models for geo-referenced failure time data 
    Ganguli Bhaswati; Wand Matt (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 ...
  • Additive models with predictors subject to measurement error 
    Ganguli Bhaswati; Staudenmayer John; Wand Matt (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 ...
  • Asymptotics and optimal bandwidth selection for highest density region estimation 
    Samworth R; Wand Matt (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 ...
  • Bayesian analysis for penalized spline regression using WinBUGS 
    Crainiceanu Ciprian; Ruppert David; Wand Matt (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 ...
  • Classifying antibodies using flow cytometry data: class prediction and class discovery 
    Salganik M.; Milford E.; Hardie D.; Shaw S.; Wand Matt (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 ...
  • Exact likelihood ratio tests for penalised splines 
    Crainiceanu Ciprian; Ruppert David; Claeskens Gerda; Wand Matt (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 ...
  • Explaining variational approximations 
    Ormerod J.; Wand Matt (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 ...
  • Explicit connections between longitudinal data analysis and kernel machines 
    Pearce N.; Wand Matt (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 ...
  • Feature significance in generalized additive models 
    Ganguli Bhaswati; Wand Matt (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 ...
  • Fisher information for generalised linear mixed models 
    Wand Matt (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.
  • General design Bayesian generalized linear mixed models 
    Zhao Yihao; Staudenmayer John; Coull B.; Wand Matt (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 ...
  • Generalised linear mixed model analysis via sequential Monte Carlo sampling 
    Fan Y.; Leslie David; Wand Matt (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 ...
  • Generalized partially linear single-index models 
    Carroll R.; Fan Jianqing; Gijbels Irene; Wand Matt (American Statistical Association, 1997)
    A semiparametric version of the generalized linear model for regression response was developed by replacing the linear combination with nonparametric components. The generalized partially linear single-index models were ...
  • Highest density difference region estimation with application to flow cytometric data 
    Duong Tarn; Koch Inge; Wand Matt (Wiley - VCH Verlag GmbH & Co. KGaA, 2009)
    Motivated by the needs of scientists using flow cytometry, we study the problem of estimating the region where two multivariate samples differ in density. We call this problem highest density difference region estimation ...
  • Marginal longitudinal semiparametric regression via penalized splines 
    Al Kadiri M; Carroll R.; Wand Matt (Elsevier BV, 2010)
    We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative ...
  • Mixed model-based additive models for sample extremes 
    Padoan S.; Wand Matt (Elsevier Inc, 2008)
    We consider additive models fitting and inference when the response variable is a sample extreme. Non-linear covariate effects are handled using the mixed model representation of penalised splines. A fitting algorithm based ...
  • Non-standard semiparametric regression via BRugs 
    Marley Jennifer; Wand Matt (American Statistical Association, 2010)
    We provide several illustrations of Bayesian semiparametric regression analyses in the BRugs package. BRugs facilitates use of the BUGS inference engine from the R computing environment and allows analyses to be managed ...
  • On semiparametric regression with O'Sullivan penalised splines 
    Wand Matt; Ormerod J. (Blackwell Publishing Ltd, 2008)
    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 ...
  • Parsimonious classification via generalised linear mixed models 
    Kauermann G.; Ormerod John; Wand Matt (Springer New York LLC, 2010)
    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 ...
  • Penalised spline support vector classifiers: computational issues 
    Ormerod John; Wand Matt; Koch Inge (Physica-Verlag Gmbh & Co, 2008)
    We study computational issues for support vector classification with penalised spline kernels. We show that, compared with traditional kernels, computational times can be drastically reduced in large problems making such ...