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Consider the standard multiple testing problem where many hypotheses are to be tested, each hypothesis...
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Consider the standard multiple testing problem where many hypotheses are to be tested, each hypothesis...
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van der Laan (2005) proposed a targeted method used to construct variable importance measures coupled...
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Likelihood-based cross-validation is a statistical tool for selecting a density estimate based on n i.i.d....
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Likelihood-based cross-validation is a statistical tool for selecting a density estimate based on n i.i.d....
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This article shows that any single-step or stepwise multiple testing procedure (asymptotically) controlling the family-wise...
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This article shows that any single-step or stepwise multiple testing procedure (asymptotically) controlling the family-wise...
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Optimal designs of dose levels in order to estimate parameters from a model for binary...
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Cross-Validated Bagged Prediction of Survival (with Sandra E. Sinisi and Romain Neugebauer), Statistical Applications in Genetics and Molecular Biology (2006)
In this article, we show how to apply our previously proposed Deletion/Substitution/Addition algorithm in the...
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van der Laan and Dudoit (2003) provide a road map for estimation and performance assessment...
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Simultaneously testing a collection of null hypotheses about a data generating distribution based on a...
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We consider the inverse problem of estimating a survival distribution when the survival times are...
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Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a...
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Analysis of viral strand sequence data and viral replication capacity could potentially lead to biological...
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The present article proposes general single-step multiple testing procedures for controlling Type I error rates...
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The present article proposes general single-step multiple testing procedures for controlling Type I error rates...
OpenURL
The present article proposes two step-down multiple testing procedures for asymptotic control of the family-wise...
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The present article proposes two step-down multiple testing procedures for asymptotic control of the family-wise...
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Simultaneously testing a collection of null hypotheses about a data generating distribution based on a...
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Many statistical problems involve the learning of an importance/effect of a variable for predicting an...
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Supervised Detection of Regulatory Motifs in DNA Sequences (with Sunduz Keles, Sandrine Dudoit, Biao Xing, and Michael B. Eisen ), Statistical Applications in Genetics and Molecular Biology (2006)
Identification of transcription factor binding sites (regulatory motifs) is a major interest in contemporary biology....
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Supervised Detection of Regulatory Motifs in DNA Sequences (with Sunduz Keles, Sandrine Dudoit, Biao Xing, and Michael B. Eisen ), Statistical Applications in Genetics and Molecular Biology (2006)
Identification of transcription factor binding sites (regulatory motifs) is a major interest in contemporary biology....
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Targeted Maximum Likelihood Learning (with Daniel Rubin), U.C. Berkeley Division of Biostatistics Working Paper Series (2006)
Suppose one observes a sample of independent and identically distributed observations from a particular data...
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Inverse probability of treatment weighting (IPTW) is frequently used to estimate the causal effects of...
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Marginal structural models (MSMs) allow one to form causal inferences from data, by specifying a...
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Consider a longitudinal observational or controlled study in which one collects chronological data over time...
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Identification of transcription factor binding sites is a major interest in contemporary biological research. A...
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We consider random design nonparametric regression when the response variable is subject to right censoring....
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Consider the standard multiple testing problem where many hypotheses are to be tested, each hypothesis...
Consider the standard multiple testing problem where many hypotheses are to be tested, each hypothesis...
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Statistical methods have rarely been applied to learn individualized treatment rules, or rules for altering...
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Many statistical methods exist that can be used to learn a predictor based on observed...
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An important class of models in causal inference are the so-called marginal structural models which...
We propose a general and formal statistical framework for the multiple tests of associations between...
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We propose a general and formal statistical framework for the multiple tests of associations between...
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Much of epidemiology and clinical medicine is focused on the estimation of treatments or interventions...
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Simultaneously testing a collection of null hypotheses about a data generating distribution based on a...
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Data Adaptive Pathway Testing (with Merrill D. Birkner and Alan E. Hubbard), U.C. Berkeley Division of Biostatistics Working Paper Series (2005)
A majority of diseases are caused by a combination of factors, for example, composite genetic...
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van der Laan (2005) proposed a method to construct variable importance measures and provided the...
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We consider the random design nonparametric regression problem when the response variable is subject to...
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Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a]...
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Estimation of Direct Causal Effects (with Maya L. Petersen), U.C. Berkeley Division of Biostatistics Working Paper Series (2005)
Many common problems in epidemiologic and clinical research involve estimating the effect of an exposure...
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Cross-validated Bagged Prediction of Survival (with Sandra E. Sinisi), U.C. Berkeley Division of Biostatistics Working Paper Series (2005)
In this article, we show how to apply our previously proposed Deletion/Substitution/Addition algorithm in the...
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Direct Effect Models (with Maya L. Petersen), U.C. Berkeley Division of Biostatistics Working Paper Series (2005)
The causal effect of a treatment on an outcome is generally mediated by several intermediate...
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Many statistical problems involve the learning of an importance/effect of a variable for predicting an...
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Providing information about the risk of disease and clinical factors that may increase or...
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Simultaneously testing multiple hypotheses is important in high-dimensional biological studies. In these situations, one is...
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We present a cross-validated bagging scheme in the context of partitioning algorithms. To explore the...
Multiple hypothesis testing problems arise frequently in biomedical and genomic research, for instance, when identifying...
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Multiple hypothesis testing problems arise frequently in biomedical and genomic research, for instance, when identifying...
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Two approaches to Causal Inference based on Marginal Structural Models (MSM) have been proposed. They...
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Robins' causal inference theory assumes existence of treatment specific counterfactual variables so that the observed...
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Cross-validated Bagged Learning (with Sandra E. Sinisi and Maya L. Petersen), U.C. Berkeley Division of Biostatistics Working Paper Series (2005)
Many applications aim to learn a high dimensional parameter of a data generating distribution based...
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Suppose that we observe a sample of independent and identically distributed realizations of a...
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Aims. This study assessed the possibility to build a prognosis predictor, based on microarray gene...
Aims. This study assessed the possibility to build a prognosis predictor, based on microarray gene...
Aims. This study assessed the possibility to build a prognosis predictor, based on non-neoplastic mucosa...
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Aims. This study assessed the possibility to build a prognosis predictor, based on non-neoplastic mucosa...
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Causal Inference based on Marginal Structural Models (MSMs) is particularly attractive to subject-matter investigators because...
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Much of clinical medicine involves choosing a future treatment plan that is expected to optimize...
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Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a...
Survival Ensembles (with Torsten Hothorn, Peter Buhlmann, Sandrine Dudoit, and Annette M. Molinaro), U.C. Berkeley Division of Biostatistics Working Paper Series (2005)
We propose a unified and flexible framework for ensemble learning in the presence of censoring....
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Survival Ensembles (with Torsten Hothorn, Peter Buhlmann, Sandrine Dudoit, and Annette M. Molinaro), U.C. Berkeley Division of Biostatistics Working Paper Series (2005)
We propose a unified and flexible framework for ensemble learning in the presence of censoring....
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Simultaneously testing a collection of null hypotheses about a data generating distribution based on a...
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Neural networks are a popular machine learning tool, particularly in applications such as the prediction...
Neural networks are a popular machine learning tool, particularly in applications such as the prediction...
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Transcriptional regulatory networks specify the interactions among regulatory genes and between regulatory genes and their...
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In this paper, we provide an overview of existing partitioning and hierarchical clustering algorithms in...
Multiple Testing Procedures and Applications to Genomics (with Merrill D. Birkner, Katherine S. Pollard, and Sandrine Dudoit), U.C. Berkeley Division of Biostatistics Working Paper Series (2005)
This chapter proposes widely applicable resampling-based single-step and stepwise multiple testing procedures (MTP) for controlling...
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Multiple Testing Procedures and Applications to Genomics (with Merrill D. Birkner, Katherine S. Pollard, and Sandrine Dudoit), U.C. Berkeley Division of Biostatistics Working Paper Series (2005)
This chapter proposes widely applicable resampling-based single-step and stepwise multiple testing procedures (MTP) for controlling...
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The present article discusses and compares multiple testing procedures (MTP) for controlling Type I error...
The present article discusses and compares multiple testing procedures (MTP) for controlling Type I error...
The Bioconductor R package multtest implements widely applicable resampling-based single-step and stepwise multiple testing procedures...
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The Bioconductor R package multtest implements widely applicable resampling-based single-step and stepwise multiple testing procedures...
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Optimal designs of dose levels in order to estimate parameters from a model for binary...
Optimal designs of dose levels in order to estimate parameters from a model for binary...
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We propose a new method for predicting censored (and non-censored) clinical outcomes from a highly-complex...
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Analysis of viral strand sequence data and viral replication capacity could potentially lead to biological...
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An important problem in epidemiology and medical research is the estimation of the causal effect...
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Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a...
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We consider the inverse problem of estimating a survival distribution when the survival times are...
We propose a class of estimators of the treatment effect on a dichotomous outcome among...
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We propose a class of estimators of the treatment effect on a dichotomous outcome among...
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The causal effect of a treatment on an outcome is generally mediated by several intermediate...
Cawley et al. (2004) have recently mapped the locations of binding sites for three transcription...
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Cawley et al. (2004) have recently mapped the locations of binding sites for three transcription...
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Regulatory Motif Finding by Logic Regression (with Sunduz Keles and Chris Vulpe), U.C. Berkeley Division of Biostatistics Working Paper Series (2004)
Multiple transcription factors coordinately control transcriptional regulation of genes in eukaryotes. Although multiple computational methods...
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Transcriptional regulation is one of the most important means of gene regulation. Uncovering transcriptional regulatory...
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In van der Laan and Dudoit (2003) we propose and theoretically study a unified loss...
The Cross-Validated Adaptive Epsilon-Net Estimator (with Sandrine Dudoit and Aad W. van der Vaart), U.C. Berkeley Division of Biostatistics Working Paper Series (2004)
Suppose that we observe a sample of independent and identically distributed realizations of a random...
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The Cross-Validated Adaptive Epsilon-Net Estimator (with Sandrine Dudoit and Aad W. van der Vaart), U.C. Berkeley Division of Biostatistics Working Paper Series (2004)
Suppose that we observe a sample of independent and identically distributed realizations of a random...
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The accompanying articles by Dudoit et al. (2003b) and van der Laan et al. (2003)...
The accompanying articles by Dudoit et al. (2003b) and van der Laan et al. (2003)...
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A simulation study was conducted to compare estimates from a naïve estimator, using standard conditional...
The present article proposes two step-down multiple testing procedures for asymptotic control of the family-wise...
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The present article proposes two step-down multiple testing procedures for asymptotic control of the family-wise...
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The present article proposes general single-step multiple testing procedures for controlling Type I error rates...
The present article proposes general single-step multiple testing procedures for controlling Type I error rates...
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Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions,...
Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions,...
In Part I of this article we propose a general cross-validation criterian for selecting among...
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In Part I of this article we propose a general cross-validation criterian for selecting among...
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In order to estimate the causal effect of treatments on an outcome of interest, one...
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Over the last two decades, non-parametric and semi-parametric approaches that adapt well known techniques such...
Over the last two decades, non-parametric and semi-parametric approaches that adapt well known techniques such...
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We propose a unified strategy for estimator construction, selection, and performance assessment in the presence...
We propose a unified strategy for estimator construction, selection, and performance assessment in the presence...
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Marginal Structural Models (MSM) have been introduced by Robins (1998a) as a powerful tool for...
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We define a general statistical framework for multiple hypothesis testing and show that the correct...
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Consider estimation of causal parameters in a marginal structural model for the discrete intensity of...
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Supervised Detection of Regulatory Motifs in DNA Sequences (with Sunduz Keles, Sandrine Dudoit, Biao Xing, and Michael B. Eisen), U.C. Berkeley Division of Biostatistics Working Paper Series (2003)
Identification of transcription factor binding sites (regulatory motifs) is a major interest in contemporary...
Supervised Detection of Regulatory Motifs in DNA Sequences (with Sunduz Keles, Sandrine Dudoit, Biao Xing, and Michael B. Eisen), U.C. Berkeley Division of Biostatistics Working Paper Series (2003)
Identification of transcription factor binding sites (regulatory motifs) is a major interest in contemporary...
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Estimators for the parameter of interest in semiparametric models often depend on a guessed model...
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Likelihood-based cross-validation is a statistical tool for selecting a density estimate based on n i.i.d....
Likelihood-based cross-validation is a statistical tool for selecting a density estimate based on n i.i.d....
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Risk estimation is an important statistical question for the purposes of selecting a good estimator...
Risk estimation is an important statistical question for the purposes of selecting a good estimator...
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Recurrent events models have lately received a lot of attention in the literature. The majority...
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Robins' causal inference theory assumes existence of treatment specific counterfactual variables so that the observed...
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Estimation for bivariate right censored data is a problem that has had much study over...
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In non-randomized treatment studies a significant problem for statisticians is determining how best to adjust...
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In biostatistics applications interest often focuses on the estimation of the distribution of a time-variable...
Case-Control Current Status Data (with Nicholas P. Jewell), U.C. Berkeley Division of Biostatistics Working Paper Series (2002)
Current status observation on survival times has recently been widely studied. An extreme form of...
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Case-Control Current Status Data (with Nicholas P. Jewell), U.C. Berkeley Division of Biostatistics Working Paper Series (2002)
Current status observation on survival times has recently been widely studied. An extreme form of...
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In point treatment marginal structural models with treatment A, outcome Y and covariates W, causal...
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Bivariate Current Status Data (with Nicholas P. Jewell), U.C. Berkeley Division of Biostatistics Working Paper Series (2002)
In many applications, it is often of interest to estimate a bivariate distribution of two...
Bivariate Current Status Data (with Nicholas P. Jewell), U.C. Berkeley Division of Biostatistics Working Paper Series (2002)
In many applications, it is often of interest to estimate a bivariate distribution of two...
Researchers working with survival data are by now adept at handling issues associated with incomplete...
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Researchers working with survival data are by now adept at handling issues associated with incomplete...
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We propose a bivariate survival function estimator for a general right censored data structure that...
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Clustering algorithms have been widely applied to gene expression data. For both hierarchical and partitioning...
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A New Partitioning Around Medoids Algorithm (with Katherine S. Pollard and Jennifer Bryan), U.C. Berkeley Division of Biostatistics Working Paper Series (2002)
Kaufman & Rousseeuw (1990) proposed a clustering algorithm Partitioning Around Medoids (PAM) which maps a...
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For statisticians analyzing medical data, a significant problem in determining the causal effect of a...
Estimation for bivariate right censored data is a problem that has had much study over...
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Many methods have been described to identify regulatory motifs in the transcription control regions of...
A great deal of recent attention has focused on the estimation of survival distributions based...
A great deal of recent attention has focused on the estimation of survival distributions based...
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Current methods for analysis of gene expression data are mostly based on clustering and classification...
We have previously described a statistical framework for using gene expression data from cDNA microarrays...
Large-scale gene expression studies are coming increasingly common as new technologies make it possible to...
We study nonparametric estimation with two types of data structures. In the first data structure...
We study nonparametric estimation with two types of data structures. In the first data structure...
Smooth Estimation of a Monotone Density (with Aad W. van der Vaart), U.C. Berkeley Division of Biostatistics Working Paper Series (2001)
We investigate the interplay of smoothness and monotonicity assumptions when estimating a density from a...
Estimation of the number of mixture components (k) is an unsolved problem. Available methods for...
Recent developments in microarray technology make it possible to capture the gene expression profiles for...
In many applications the observed data can be viewed as a censored high dimensional full...
In this paper we develop a locally efficient one-step estimator of a multivariate survival function...
In biostatistical applications interest often focuses on the estimation of the distribution of time T...
In biostatistical applications interest often focuses on the estimation of the distribution of time T...
We study nonparametric estimation with two types of data structures. In the first data structure...
We study nonparametric estimation with two types of data structures. In the first data structure...
In biostatistical applications interest often focuses on the estimation of the distribution of a time-until-event...
The California Partners' Study is an ongoing investigation of heterosexual HIV transmission in partners of...
Consider k equal size treatment groups and let the outcome of interest be a survival...
Inference with Bivariate Truncated Data (with Christopher M. Quale), U.C. Berkeley Division of Biostatistics Working Paper Series (1998)
In this paper we build on previous work for estimation of the bivariate distribution of...
Zhao and Tsiatis (1997) consider the problem of estimation of the distribution of the quality...
In many observational studies one is concerned with comparing treatment specific survival distributions in the...
The Kaplan-Meier estimator of a survival function is well known to be asymp- totically efficient...
The Kaplan-Meier estimator of a survival function is well known to be asymp- totically efficient...
We study efficient nonparametric maximum likelihood estimation of the distribution of onset and lifetime associated...
We study efficient nonparametric maximum likelihood estimation of the distribution of onset and lifetime associated...
In many biostatistical applications one is concerned with estimating the distribution of a survival time...
For many sources of survival data, there is a delay between the recording of vital...
In biostatistical applications interest often focuses on the estimation of the distribution of a failure...
In biostatistical applications interest often focuses on the estimation of the distribution of time T...
In estimation of a survival function, current status data arises when the only information available...
In estimation of a survival function, current status data arises when the only information available...
In biostatistical applications, interest often focuses on the estimation of the distribution of time T...
In this paper, the NPMLE in the one-dimensional line segment problem is defined and studied,...
We consider nonparametric missing data models for which the censoring mechanism satisfies coarsening at random...
Randomly left or right truncated observations occur when one is concerned with estimation of the...
In biostatistical applications interest is often focused on the estimation of the distribution of time...
In biostatistical applications interest is often focused on the estimation of the distribution of time...
A large number of proposals for estimating the bivariate survival function under random censoring has...
In estimation of a survival function, current status data arises when the only information available...
In estimation of a survival function, current status data arises when the only information available...
A method is given for proving efficiency of NPMLE directly linked to empirical process theory....