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142,402
Influence diagnostics for the normal linear model with censored data
 Australian Journal of Statistics
, 1990
"... Methods of detecting influential observations for the normal model for censored data are proposed. These methods include onestep deletion methods, deletion of observations and the empirical influence function. Emphasis is placed on assessing the impact that a single ohservatioii has on the estim ..."
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Cited by 2 (0 self)
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Methods of detecting influential observations for the normal model for censored data are proposed. These methods include onestep deletion methods, deletion of observations and the empirical influence function. Emphasis is placed on assessing the impact that a single ohservatioii has
Evaluating the Accuracy of SamplingBased Approaches to the Calculation of Posterior Moments
 IN BAYESIAN STATISTICS
, 1992
"... Data augmentation and Gibbs sampling are two closely related, samplingbased approaches to the calculation of posterior moments. The fact that each produces a sample whose constituents are neither independent nor identically distributed complicates the assessment of convergence and numerical accurac ..."
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Cited by 604 (12 self)
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accuracy of the approximations to the expected value of functions of interest under the posterior. In this paper methods from spectral analysis are used to evaluate numerical accuracy formally and construct diagnostics for convergence. These methods are illustrated in the normal linear model
Consistency of Bayes factors for intrinsic priors in normal linear models
"... Abstract The JeffreysLindley paradox refers to the wellknown fact that a sharp null hypothesis on the normal mean parameter is always accepted when the variance of the conjugate prior goes to infinity, thus implying that the resulting Bayesian procedure is not consistent, and that some limiting f ..."
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forms of proper prior distributions are not necessarily suitable for testing problems. Intrinsic priors, which are limits of proper priors, have been proved to be extremely useful for testing problems, and, in particular, for testing hypothesis on the regression coefficients of normal linear models
Limma: linear models for microarray data
 Bioinformatics and Computational Biology Solutions using R and Bioconductor
, 2005
"... This free opensource software implements academic research by the authors and coworkers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents ..."
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Cited by 774 (13 self)
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This free opensource software implements academic research by the authors and coworkers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Contents
CREDIBILITY FOR CLASSIFICATION RATEMAKING VIA THE HIERARCHICAL NORMAL LINEAR MODEL
"... In the past twenty years there has been ever increasing improvement in the techniques of classification ratemaking. Most of this has centered around improvements in credibility procedures and most of the improvements have been due to incorporating aspects of Bayesian analysis. In this paper, I attem ..."
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Cited by 4 (0 self)
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description and analysis of the hierarchical normal linear model (HNLM). Included are point estimation, estimation of the error in the estimator, and prediction intervals for future losses. The last two items are of special interest since current credibility procedures provide little insight with respect
Exploration, normalization, and summaries of high density oligonucleotide array probe level data.
 Biostatistics,
, 2003
"... SUMMARY In this paper we report exploratory analyses of highdensity oligonucleotide array data from the Affymetrix GeneChip R system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of f ..."
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Cited by 854 (33 self)
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and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spikein data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model
Longitudinal data analysis using generalized linear models”.
 Biometrika,
, 1986
"... SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating ..."
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Cited by 1526 (8 self)
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SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence
Lambertian Reflectance and Linear Subspaces
, 2000
"... We prove that the set of all reflectance functions (the mapping from surface normals to intensities) produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a wi ..."
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Cited by 526 (20 self)
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We prove that the set of all reflectance functions (the mapping from surface normals to intensities) produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a
Regularization paths for generalized linear models via coordinate descent
, 2009
"... We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, twoclass logistic regression, and multinomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge regression) and mixtures of the two (the elastic ..."
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Cited by 724 (15 self)
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We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, twoclass logistic regression, and multinomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge regression) and mixtures of the two (the
Linear models and empirical bayes methods for assessing differential expression in microarray experiments.
 Stat. Appl. Genet. Mol. Biol.
, 2004
"... Abstract The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated twocolor experiment using a simple hierarchical parametric model. ..."
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Cited by 1321 (24 self)
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. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples. The model is reset in the context of general linear models with arbitrary coefficients and contrasts
Results 1  10
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142,402