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GEPAS: a webbased resource for microarray gene expression data analysis
 Nucleic Acids Res
, 2003
"... We present a webbased pipeline for microarray gene expression profile analysis, GEPAS, which stands for Gene Expression Profile Analysis Suite ..."
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Cited by 63 (31 self)
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We present a webbased pipeline for microarray gene expression profile analysis, GEPAS, which stands for Gene Expression Profile Analysis Suite
Variable Selection for Cox's Proportional Hazards Model and Frailty Model
 ANNALS OF STATISTICS
, 2002
"... A class of variable selection procedures for parametric models via nonconcave penalized likelihood was proposed in Fan and Li (2001a). It has been shown there that the resulting procedures perform as well as if the subset of significant variables were known in advance. Such a property is called an o ..."
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Cited by 59 (13 self)
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A class of variable selection procedures for parametric models via nonconcave penalized likelihood was proposed in Fan and Li (2001a). It has been shown there that the resulting procedures perform as well as if the subset of significant variables were known in advance. Such a property is called an oracle property. The proposed procedures were illustrated in the context of linear regression, robust linear regression and generalized linear models. In this paper, the nonconcave penalized likelihood approach is extended further to the Cox proportional hazards model and the Cox proportional hazards frailty model, two commonly used semiparametric models in survival analysis. As a result, new variable selection procedures for these two commonlyused models are proposed. It is demonstrated how the rates of convergence depend on the regularization parameter in the penalty function. Further, with a proper choice of the regularization parameter and the penalty function, the proposed estimators possess an oracle property. Standard error formulae are derived and their accuracies are empirically tested. Simulation studies show that the proposed procedures are more stable in prediction and more effective in computation than the best subset variable selection, and they reduce model complexity as effectively as the best subset variable selection. Compared with the LASSO, which is the penalized likelihood method with the L1penalty, proposed by Tibshirani, the newly proposed approaches have better theoretic properties and finite sample performance.
Modeling Regression Error with a Mixture of Polya Trees
 Journal of the American Statistical Association
, 2001
"... We model the error distribution in the standard linear model as a mixture of absolutely continuous Polya trees constrained to have median zero. By considering a mixture, we smooth out the partitioning e ects of a simple Polya tree and the predictive error density has a derivative everywhere except z ..."
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Cited by 27 (3 self)
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We model the error distribution in the standard linear model as a mixture of absolutely continuous Polya trees constrained to have median zero. By considering a mixture, we smooth out the partitioning e ects of a simple Polya tree and the predictive error density has a derivative everywhere except zero. The error distribution is centered around a standard parametric family of distributions and may therefore be viewed as a generalization of standard models in which important, datadriven features, such as skewness and multimodality, are allowed. By marginalizing the Polya tree exact inference is possible up to MCMC error.
New challenges in gene expression data analysis and the extended GEPAS
 Nucleic Acids Res
, 2004
"... Since the first papers published in the late nineties, including, for the first time, a comprehensive analysis of microarray data, the number of questions that have been addressed through this technique have both increased and diversified. Initially, interest focussed on genes coexpressing across se ..."
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Cited by 23 (11 self)
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Since the first papers published in the late nineties, including, for the first time, a comprehensive analysis of microarray data, the number of questions that have been addressed through this technique have both increased and diversified. Initially, interest focussed on genes coexpressing across sets of experimental conditions, implying, essentially, the use of clustering techniques. Recently, however, interest has focussed more on finding genes differentially expressed among distinct classes of experiments, or correlated to diverse clinical outcomes, as well as in building predictors. In addition to this, the availability of accurate genomic data and the recent implementation of CGH arrays has made mapping expression and genomic data on the chromosomes possible. There is also a clear demand for methods that allow the automatic transfer of biological information to the results of microarray experiments. Different initiatives, such as the Gene Ontology (GO) consortium, pathways databases, protein functional motifs, etc., provide curated annotations for genes. Whereas many resources on the web focus mainly on clustering methods, GEPAS has evolved to cope with the aforementioned new challenges that have recently arisen in the field of microarray data analysis. The webbased pipeline for microarray gene expression data, GEPAS, is available at
Active set and EM algorithms for logconcave densities based on complete and censored data
, 2007
"... Abstract. We develop an active set algorithm for the maximum likelihood estimation of a log–concave density based on complete data. Building on this fast algorithm, we introduce an EM algorithm to treat arbitrarily censored data, e.g. right–censored or interval–censored data. 1 ..."
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Cited by 16 (7 self)
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Abstract. We develop an active set algorithm for the maximum likelihood estimation of a log–concave density based on complete data. Building on this fast algorithm, we introduce an EM algorithm to treat arbitrarily censored data, e.g. right–censored or interval–censored data. 1
Utility of HumanComputer Interactions: Toward a Science of Preference Measurement
"... The success of a computer system depends upon a user choosing it, but the field of HumanComputer Interaction has little ability to predict this user choice. We present a new method that measures user choice, and quantifies it as a measure of utility. Our method has two core features. First, it intr ..."
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Cited by 16 (0 self)
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The success of a computer system depends upon a user choosing it, but the field of HumanComputer Interaction has little ability to predict this user choice. We present a new method that measures user choice, and quantifies it as a measure of utility. Our method has two core features. First, it introduces an economic definition of utility, one that we can operationalize through economic experiments. Second, we employ a novel method of crowdsourcing that enables the collection of thousands of economic judgments from real users. ACM Classification: H5.m. Information interfaces and presentation: User Interfaces.
Modelling markers of disease progression by a hidden Markov process: application to characterising CD4 cell decline
"... INSERM, U170, 16 avenue Paul Vaillant Couturier, 94807 Villejuif, France Email: richardson@vjf.inserm.fr z Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, GA, USA . 1 1 Introduction Multistate models have been increasingly used to model the natural hi ..."
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Cited by 6 (2 self)
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INSERM, U170, 16 avenue Paul Vaillant Couturier, 94807 Villejuif, France Email: richardson@vjf.inserm.fr z Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, GA, USA . 1 1 Introduction Multistate models have been increasingly used to model the natural history of chronic, viral or infectious diseases as well as to characterize the followup of patients under varied clinical protocols (Kay, 1986 for hepatocellular carcinoma; Andersen, Hansen and Keiding, 1991 for liver cirrhosis evolution; Andersen, 1988 for diabetic nephropathy, Marshall and Jones, 1995 for diabetic retinopathy; Sharples, 1993 for coronary heart disease after transplantation...). The definitions of the states often use discretization of continuous markers (serum alphafetoprotein levels as cancer marker (Kay, 1986), decline of CD4 cell count as HIV progression marker (Longini et al (1989, 1991), Taylor et a
BioMed Central
, 2006
"... A novel approach to phylogenetic tree construction using stochastic optimization and clustering ..."
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Cited by 6 (3 self)
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A novel approach to phylogenetic tree construction using stochastic optimization and clustering
A Bayesian Semiparametric AFT Model for Interval Censored Data
, 2001
"... We model the baseline distribution in the accelerated failuretime model as a mixture of Dirichlet processes for interval censored data. This mixture is distinct from Dirichlet process mixtures and can be viewed as a simple extension of existing parametric models. We introduce a novel MCMC scheme fo ..."
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Cited by 4 (0 self)
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We model the baseline distribution in the accelerated failuretime model as a mixture of Dirichlet processes for interval censored data. This mixture is distinct from Dirichlet process mixtures and can be viewed as a simple extension of existing parametric models. We introduce a novel MCMC scheme for the purpose of making posterior inferences and illustrate our methods with several real examples.
The singularity of the information matrix of the mixed proportional hazard model
 Econometrica
, 2003
"... This paper presents new identification conditions for the mixed proportional hazard model. In particular, the baseline hazard is assumed to be bounded away from 0 and ∞ near t = 0. These conditions ensure that the information matrix is nonsingular. The paper also presents an estimator for the mixed ..."
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Cited by 3 (1 self)
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This paper presents new identification conditions for the mixed proportional hazard model. In particular, the baseline hazard is assumed to be bounded away from 0 and ∞ near t = 0. These conditions ensure that the information matrix is nonsingular. The paper also presents an estimator for the mixed proportional hazard model that converges at rate N −1/2.