Search Results - "Wand, M. P."

Simple Marginally Noninformative Prior Distributions for Covariance Matrices by Huang, Alan, Wand, M. P.

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Explaining Variational Approximations by Ormerod, J. T., Wand, M. P.

“…Variational approximations facilitate approximate inference for the parameters in complex statistical models and provide fast, deterministic alternatives to…”
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Variational Bayesian Inference for Parametric and Nonparametric Regression With Missing Data by Faes, C., Ormerod, J. T., Wand, M. P.

“…Bayesian hierarchical models are attractive structures for conducting regression analyses when the data are subject to missingness. However, the requisite…”
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Geoadditive models by Kammann, E. E., Wand, M. P.

“…A study into geographical variability of reproductive health outcomes (e.g. birth weight) in Upper Cape Cod, Massachusetts, USA, benefits from geostatistical…”
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ASYMPTOTICS AND OPTIMAL BANDWIDTH SELECTION FOR HIGHEST DENSITY REGION ESTIMATION by Samworth, R. J., Wand, M. P.

“…We study kernel estimation of highest-density regions (HDR). Our main contributions are two-fold. First, we derive a uniform-in-bandwidth asymptotic…”
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Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models via Message Passing by Wand, M. P.

“…We show how the notion of message passing can be used to streamline the algebra and computer coding for fast approximate inference in large Bayesian…”
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ASYMPTOTIC NORMALITY AND VALID INFERENCE FOR GAUSSIAN VARIATIONAL APPROXIMATION by Hall, Peter, Pham, Tung, Wand, M. P., Wang, S. S. J.

“…We derive the precise asymptotic distributional behavior of Gaussian variational approximate estimators of the parameters in a single-predictor Poisson mixed…”
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Generalized Partially Linear Single-Index Models by Carroll, R. J., Fan, Jianqing, Gijbels, Irène, Wand, M. P.

“…The typical generalized linear model for a regression of a response Y on predictors (X, Z) has conditional mean function based on a linear combination of (X,…”
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Smoothing and mixed models by Wand, M. P.

“…SummarySmoothing methods that use. basis functions with penalisation can be formulated as maximum likelihood estimators and best predictors in a mixed model…”
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Exact likelihood ratio tests for penalised splines by Crainiceanu, Ciprian, Ruppert, David, Claeskens, Gerda, Wand, M. P.

“…Penalised-spline-based additive models allow a simple mixed model representation where the variance components control departures from linear models. The…”
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Loss of mammary epithelial prolactin receptor delays tumor formation by reducing cell proliferation in low-grade preinvasive lesions by OAKES, S. R, ROBERTSON, F. G, KENCH, J. G, GARDINER-GARDEN, M, WAND, M. P, GREEN, J. E, ORMANDY, C. J

“…Top quartile serum prolactin levels confer a twofold increase in the relative risk of developing breast cancer. Prolactin exerts this effect at an ill defined…”
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Multivariate Locally Weighted Least Squares Regression by Ruppert, D., Wand, M. P.

“…Nonparametric regression using locally weighted least squares was first discussed by Stone and by Cleveland. Recently, it was shown by Fan and by Fan and…”
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An Effective Bandwidth Selector for Local Least Squares Regression by Ruppert, D., Sheather, S. J., Wand, M. P.

“…Local least squares kernel regression provides an appealing solution to the nonparametric regression, or "scatterplot smoothing," problem, as demonstrated by…”
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Simple fitting of subject-specific curves for longitudinal data by Durbán, M., Harezlak, J., Wand, M. P., Carroll, R. J.

“…We present a simple semiparametric model for fitting subject‐specific curves for longitudinal data. Individual curves are modelled as penalized splines with…”
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Data-Based Choice of Histogram Bin Width by Wand, M. P.

“…The most important parameter of a histogram is the bin width because it controls the tradeoff between presenting a picture with too much detail…”
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Using Infer.NET for Statistical Analyses by Wang, S. S. J., Wand, M. P.

“…We demonstrate and critique the new Bayesian inference package Infer.NET in terms of its capacity for statistical analyses. Infer.NET differs from the…”
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Local Polynomial Kernel Regression for Generalized Linear Models and Quasi-Likelihood Functions by Fan, Jianqing, Heckman, Nancy E., Wand, M. P.

“…We investigate the extension of the nonparametric regression technique of local polynomial fitting with a kernel weight to generalized linear models and…”
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Highest Density Difference Region Estimation with Application to Flow Cytometric Data by Duong, Tarn, Koch, Inge, Wand, M. P.

“…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…”
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Penalized Splines and Reproducing Kernel Methods by Pearce, N. D, Wand, M. P

“…Two data analytic research areas-penalized splines and reproducing kernel methods-have become very vibrant since the mid-1990s. This article shows how the…”
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A General Projection Framework for Constrained Smoothing by Mammen, E., Marron, J. S., Turlach, B. A., Wand, M. P.

“…There are a wide array of smoothing methods available for finding structure in data. A general framework is developed which shows that many of these can be…”
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