Link
We propose a general and formal statistical framework for the multiple tests of associations between...
Biological Sequence Analysis
Link
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....
Link
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...
Categorical Data Analysis
PDF
Simultaneously testing a collection of null hypotheses about a data generating distribution based on a...
PDF
Optimal designs of dose levels in order to estimate parameters from a model for binary...
Link
Optimal designs of dose levels in order to estimate parameters from a model for binary...
Link
We propose a class of estimators of the treatment effect on a dichotomous outcome among...
PDF
We propose a class of estimators of the treatment effect on a dichotomous outcome among...
PDF
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...
Link
Consider k equal size treatment groups and let the outcome of interest be a survival...
Clinical Epidemiology
PDF
Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a...
Clinical Trials
Link
We propose a class of estimators of the treatment effect on a dichotomous outcome among...
PDF
We propose a class of estimators of the treatment effect on a dichotomous outcome among...
PDF
The causal effect of a treatment on an outcome is generally mediated by several intermediate...
PDF
A simulation study was conducted to compare estimates from a naïve estimator, using standard conditional...
PDF
In order to estimate the causal effect of treatments on an outcome of interest, one...
PDF
For statisticians analyzing medical data, a significant problem in determining the causal effect of a...
Computation
PDF
Simultaneously testing a collection of null hypotheses about a data generating distribution based on a...
PDF
In this paper, we provide an overview of existing partitioning and hierarchical clustering algorithms in...
PDF
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...
PDF
An important problem in epidemiology and medical research is the estimation of the causal effect...
PDF
Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a...
PDF
In van der Laan and Dudoit (2003) we propose and theoretically study a unified loss...
PDF
In Part I of this article we propose a general cross-validation criterian for selecting among...
PDF
Marginal Structural Models (MSM) have been introduced by Robins (1998a) as a powerful tool for...
PDF
We define a general statistical framework for multiple hypothesis testing and show that the correct...
PDF
Consider estimation of causal parameters in a marginal structural model for the discrete intensity of...
PDF
In non-randomized treatment studies a significant problem for statisticians is determining how best to adjust...
PDF
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...
Link
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...
PDF
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...
PDF
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....
PDF
Analysis of viral strand sequence data and viral replication capacity could potentially lead to biological...
PDF
In this paper, we provide an overview of existing partitioning and hierarchical clustering algorithms in...
PDF
Current methods for analysis of gene expression data are mostly based on clustering and classification...
Link
We have previously described a statistical framework for using gene expression data from cDNA microarrays...
Link
Large-scale gene expression studies are coming increasingly common as new technologies make it possible to...
Link
Recent developments in microarray technology make it possible to capture the gene expression profiles for...
Design of Experiments and Sample Surveys
Link
Recent developments in microarray technology make it possible to capture the gene expression profiles for...
Disease Modeling
PDF
Analysis of viral strand sequence data and viral replication capacity could potentially lead to biological...
PDF
Aims. This study assessed the possibility to build a prognosis predictor, based on microarray gene...
PDF
Aims. This study assessed the possibility to build a prognosis predictor, based on non-neoplastic mucosa...
PDF
Analysis of viral strand sequence data and viral replication capacity could potentially lead to biological...
Link
We study efficient nonparametric maximum likelihood estimation of the distribution of onset and lifetime associated...
Epidemiology
PDF
Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a...
PDF
Marginal structural models (MSMs) allow one to form causal inferences from data, by specifying a...
PDF
Much of epidemiology and clinical medicine is focused on the estimation of treatments or interventions...
PDF
Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a]...
PDF
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...
PDF
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...
PDF
Causal Inference based on Marginal Structural Models (MSMs) is particularly attractive to subject-matter investigators because...
PDF
Much of clinical medicine involves choosing a future treatment plan that is expected to optimize...
PDF
Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a...
PDF
An important problem in epidemiology and medical research is the estimation of the causal effect...
PDF
Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a...
Link
We propose a class of estimators of the treatment effect on a dichotomous outcome among...
PDF
We propose a class of estimators of the treatment effect on a dichotomous outcome among...
PDF
The causal effect of a treatment on an outcome is generally mediated by several intermediate...
PDF
Cawley et al. (2004) have recently mapped the locations of binding sites for three transcription...
PDF
In non-randomized treatment studies a significant problem for statisticians is determining how best to adjust...
Link
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...
PDF
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...
Link
Researchers working with survival data are by now adept at handling issues associated with incomplete...
PDF
Researchers working with survival data are by now adept at handling issues associated with incomplete...
PDF
For statisticians analyzing medical data, a significant problem in determining the causal effect of a...
Link
We study efficient nonparametric maximum likelihood estimation of the distribution of onset and lifetime associated...
General Biostatistics
PDF
Many statistical problems involve the learning of an importance/effect of a variable for predicting an...
PDF
Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a...
PDF
This article shows that any single-step or stepwise multiple testing procedure (asymptotically) controlling the family-wise...
PDF
We propose a general and formal statistical framework for the multiple tests of associations between...
PDF
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...
PDF
van der Laan (2005) proposed a method to construct variable importance measures and provided the...
PDF
Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a]...
PDF
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...
PDF
Many statistical problems involve the learning of an importance/effect of a variable for predicting an...
PDF
Simultaneously testing multiple hypotheses is important in high-dimensional biological studies. In these situations, one is...
PDF
Multiple hypothesis testing problems arise frequently in biomedical and genomic research, for instance, when identifying...
PDF
Suppose that we observe a sample of independent and identically distributed realizations of a...
PDF
Causal Inference based on Marginal Structural Models (MSMs) is particularly attractive to subject-matter investigators because...
PDF
Neural networks are a popular machine learning tool, particularly in applications such as the prediction...
Genetics
PDF
We propose a general and formal statistical framework for the multiple tests of associations between...
Human Genetics
PDF
Simultaneously testing multiple hypotheses is important in high-dimensional biological studies. In these situations, one is...
PDF
Aims. This study assessed the possibility to build a prognosis predictor, based on non-neoplastic mucosa...
PDF
In this paper, we provide an overview of existing partitioning and hierarchical clustering algorithms in...
PDF
Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions,...
PDF
We propose a unified strategy for estimator construction, selection, and performance assessment in the presence...
PDF
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...
PDF
Clustering algorithms have been widely applied to gene expression data. For both hierarchical and partitioning...
PDF
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...
PDF
Many methods have been described to identify regulatory motifs in the transcription control regions of...
Link
Recent developments in microarray technology make it possible to capture the gene expression profiles for...
Laboratory and Basic Science Research
PDF
Identification of transcription factor binding sites is a major interest in contemporary biological research. A...
PDF
We propose a general and formal statistical framework for the multiple tests of associations between...
PDF
Simultaneously testing multiple hypotheses is important in high-dimensional biological studies. In these situations, one is...
PDF
Multiple hypothesis testing problems arise frequently in biomedical and genomic research, for instance, when identifying...
PDF
The Bioconductor R package multtest implements widely applicable resampling-based single-step and stepwise multiple testing procedures...
Longitudinal Data Analysis and Time Series
PDF
Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a...
PDF
Statistical methods have rarely been applied to learn individualized treatment rules, or rules for altering...
PDF
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...
PDF
Two approaches to Causal Inference based on Marginal Structural Models (MSM) have been proposed. They...
PDF
Causal Inference based on Marginal Structural Models (MSMs) is particularly attractive to subject-matter investigators because...
PDF
Transcriptional regulatory networks specify the interactions among regulatory genes and between regulatory genes and their...
PDF
The causal effect of a treatment on an outcome is generally mediated by several intermediate...
PDF
A simulation study was conducted to compare estimates from a naïve estimator, using standard conditional...
PDF
Marginal Structural Models (MSM) have been introduced by Robins (1998a) as a powerful tool for...
PDF
Consider estimation of causal parameters in a marginal structural model for the discrete intensity of...
PDF
Robins' causal inference theory assumes existence of treatment specific counterfactual variables so that the observed...
PDF
For statisticians analyzing medical data, a significant problem in determining the causal effect of a...
Link
In this paper we develop a locally efficient one-step estimator of a multivariate survival function...
Link
For many sources of survival data, there is a delay between the recording of vital...
Link
In biostatistical applications interest often focuses on the estimation of the distribution of a failure...
Loss-Based Estimation with Cross-Validation
Link
Likelihood-based cross-validation is a statistical tool for selecting a density estimate based on n i.i.d....
Link
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....
Link
Neural networks are a popular machine learning tool, particularly in applications such as the prediction...
Link
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...
Link
Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions,...
Link
In Part I of this article we propose a general cross-validation criterian for selecting among...
Link
Over the last two decades, non-parametric and semi-parametric approaches that adapt well known techniques such...
Link
We propose a unified strategy for estimator construction, selection, and performance assessment in the presence...
Link
Likelihood-based cross-validation is a statistical tool for selecting a density estimate based on n i.i.d....
Link
Risk estimation is an important statistical question for the purposes of selecting a good estimator...
Medical Specialties
PDF
Aims. This study assessed the possibility to build a prognosis predictor, based on microarray gene...
PDF
Aims. This study assessed the possibility to build a prognosis predictor, based on non-neoplastic mucosa...
PDF
Cawley et al. (2004) have recently mapped the locations of binding sites for three transcription...
Microarray Data Analysis
Link
Aims. This study assessed the possibility to build a prognosis predictor, based on microarray gene...
Link
Aims. This study assessed the possibility to build a prognosis predictor, based on non-neoplastic mucosa...
Link
Cawley et al. (2004) have recently mapped the locations of binding sites for three transcription...
Microarrays
PDF
In this paper, we provide an overview of existing partitioning and hierarchical clustering algorithms in...
PDF
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...
PDF
Transcriptional regulation is one of the most important means of gene regulation. Uncovering transcriptional regulatory...
PDF
Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions,...
PDF
Clustering algorithms have been widely applied to gene expression data. For both hierarchical and partitioning...
PDF
Many methods have been described to identify regulatory motifs in the transcription control regions of...
PDF
Current methods for analysis of gene expression data are mostly based on clustering and classification...
Link
We have previously described a statistical framework for using gene expression data from cDNA microarrays...
Link
Recent developments in microarray technology make it possible to capture the gene expression profiles for...
Multiple Hypothesis Testing
Link
The present article proposes two step-down multiple testing procedures for asymptotic control of the family-wise...
Link
The present article proposes general single-step multiple testing procedures for controlling Type I error rates...
Link
This article shows that any single-step or stepwise multiple testing procedure (asymptotically) controlling the family-wise...
Link
Consider the standard multiple testing problem where many hypotheses are to be tested, each hypothesis...
Link
Consider the standard multiple testing problem where many hypotheses are to be tested, each hypothesis...
Link
Multiple hypothesis testing problems arise frequently in biomedical and genomic research, for instance, when identifying...
Link
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...
Link
The present article discusses and compares multiple testing procedures (MTP) for controlling Type I error...
Link
The accompanying articles by Dudoit et al. (2003b) and van der Laan et al. (2003)...
Link
The present article proposes two step-down multiple testing procedures for asymptotic control of the family-wise...
Link
The present article proposes general single-step multiple testing procedures for controlling Type I error rates...
Multivariate Analysis
PDF
Analysis of viral strand sequence data and viral replication capacity could potentially lead to biological...
PDF
van der Laan (2005) proposed a targeted method used to construct variable importance measures coupled...
PDF
We propose a general and formal statistical framework for the multiple tests of associations between...
PDF
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...
PDF
van der Laan (2005) proposed a method to construct variable importance measures and provided the...
PDF
Many statistical problems involve the learning of an importance/effect of a variable for predicting an...
PDF
Multiple hypothesis testing problems arise frequently in biomedical and genomic research, for instance, when identifying...
PDF
Aims. This study assessed the possibility to build a prognosis predictor, based on microarray gene...
PDF
Aims. This study assessed the possibility to build a prognosis predictor, based on non-neoplastic mucosa...
PDF
Causal Inference based on Marginal Structural Models (MSMs) is particularly attractive to subject-matter investigators because...
PDF
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....
PDF
In this paper, we provide an overview of existing partitioning and hierarchical clustering algorithms in...
PDF
The Bioconductor R package multtest implements widely applicable resampling-based single-step and stepwise multiple testing procedures...
PDF
Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions,...
PDF
We propose a unified strategy for estimator construction, selection, and performance assessment in the presence...
PDF
Clustering algorithms have been widely applied to gene expression data. For both hierarchical and partitioning...
PDF
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...
PDF
Current methods for analysis of gene expression data are mostly based on clustering and classification...
Link
We have previously described a statistical framework for using gene expression data from cDNA microarrays...
Link
Large-scale gene expression studies are coming increasingly common as new technologies make it possible to...
Link
Recent developments in microarray technology make it possible to capture the gene expression profiles for...
Link
In this paper we develop a locally efficient one-step estimator of a multivariate survival function...
Statistical Computing
Link
The Bioconductor R package multtest implements widely applicable resampling-based single-step and stepwise multiple testing procedures...
Statistical Models
PDF
Analysis of viral strand sequence data and viral replication capacity could potentially lead to biological...
PDF
van der Laan and Dudoit (2003) provide a road map for estimation and performance assessment...
PDF
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...
PDF
Many statistical methods exist that can be used to learn a predictor based on observed...
PDF
An important class of models in causal inference are the so-called marginal structural models which...
PDF
We propose a general and formal statistical framework for the multiple tests of associations between...
PDF
Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a]...
PDF
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...
PDF
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...
PDF
Providing information about the risk of disease and clinical factors that may increase or...
PDF
Simultaneously testing multiple hypotheses is important in high-dimensional biological studies. In these situations, one is...
PDF
We present a cross-validated bagging scheme in the context of partitioning algorithms. To explore the...
PDF
Multiple hypothesis testing problems arise frequently in biomedical and genomic research, for instance, when identifying...
PDF
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...
PDF
Causal Inference based on Marginal Structural Models (MSMs) is particularly attractive to subject-matter investigators because...
PDF
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....
PDF
An important problem in epidemiology and medical research is the estimation of the causal effect...
PDF
Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a...
PDF
We consider the inverse problem of estimating a survival distribution when the survival times are...
Link
We propose a class of estimators of the treatment effect on a dichotomous outcome among...
PDF
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...
PDF
Transcriptional regulation is one of the most important means of gene regulation. Uncovering transcriptional regulatory...
PDF
In van der Laan and Dudoit (2003) we propose and theoretically study a unified loss...
PDF
A simulation study was conducted to compare estimates from a naïve estimator, using standard conditional...
PDF
In Part I of this article we propose a general cross-validation criterian for selecting among...
PDF
In order to estimate the causal effect of treatments on an outcome of interest, one...
PDF
Over the last two decades, non-parametric and semi-parametric approaches that adapt well known techniques such...
PDF
We propose a unified strategy for estimator construction, selection, and performance assessment in the presence...
PDF
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...
PDF
Estimators for the parameter of interest in semiparametric models often depend on a guessed model...
PDF
Recurrent events models have lately received a lot of attention in the literature. The majority...
PDF
Estimation for bivariate right censored data is a problem that has had much study over...
PDF
In non-randomized treatment studies a significant problem for statisticians is determining how best to adjust...
PDF
In biostatistics applications interest often focuses on the estimation of the distribution of a time-variable...
PDF
In point treatment marginal structural models with treatment A, outcome Y and covariates W, causal...
PDF
For statisticians analyzing medical data, a significant problem in determining the causal effect of a...
PDF
Many methods have been described to identify regulatory motifs in the transcription control regions of...
Link
A great deal of recent attention has focused on the estimation of survival distributions based...
Link
In this paper we develop a locally efficient one-step estimator of a multivariate survival function...
Link
Zhao and Tsiatis (1997) consider the problem of estimation of the distribution of the quality...
Link
For many sources of survival data, there is a delay between the recording of vital...
Link
In biostatistical applications interest often focuses on the estimation of the distribution of a failure...
Statistical Theory and Methods
PDF
Simultaneously testing a collection of null hypotheses about a data generating distribution based on a...
PDF
The present article proposes two step-down multiple testing procedures for asymptotic control of the family-wise...
PDF
The present article proposes general single-step multiple testing procedures for controlling Type I error rates...
PDF
We consider the inverse problem of estimating a survival distribution when the survival times are...
PDF
Simultaneously testing a collection of null hypotheses about a data generating distribution based on a...
PDF
This article shows that any single-step or stepwise multiple testing procedure (asymptotically) controlling the family-wise...
PDF
Likelihood-based cross-validation is a statistical tool for selecting a density estimate based on n i.i.d....
PDF
Consider the standard multiple testing problem where many hypotheses are to be tested, each hypothesis...
PDF
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...
PDF
Inverse probability of treatment weighting (IPTW) is frequently used to estimate the causal effects of...
PDF
Marginal structural models (MSMs) allow one to form causal inferences from data, by specifying a...
PDF
Consider a longitudinal observational or controlled study in which one collects chronological data over time...
PDF
We consider random design nonparametric regression when the response variable is subject to right censoring....
PDF
Consider the standard multiple testing problem where many hypotheses are to be tested, each hypothesis...
PDF
We propose a general and formal statistical framework for the multiple tests of associations between...
PDF
Simultaneously testing a collection of null hypotheses about a data generating distribution based on a...
PDF
We consider the random design nonparametric regression problem when the response variable is subject to...
PDF
Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a]...
PDF
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...
PDF
Simultaneously testing multiple hypotheses is important in high-dimensional biological studies. In these situations, one is...
PDF
We present a cross-validated bagging scheme in the context of partitioning algorithms. To explore the...
PDF
Multiple hypothesis testing problems arise frequently in biomedical and genomic research, for instance, when identifying...
PDF
Robins' causal inference theory assumes existence of treatment specific counterfactual variables so that the observed...
PDF
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...
PDF
Causal Inference based on Marginal Structural Models (MSMs) is particularly attractive to subject-matter investigators because...
PDF
Simultaneously testing a collection of null hypotheses about a data generating distribution based on a...
PDF
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...
PDF
The present article discusses and compares multiple testing procedures (MTP) for controlling Type I error...
PDF
The Bioconductor R package multtest implements widely applicable resampling-based single-step and stepwise multiple testing procedures...
PDF
Optimal designs of dose levels in order to estimate parameters from a model for binary...
Link
Optimal designs of dose levels in order to estimate parameters from a model for binary...
PDF
We propose a new method for predicting censored (and non-censored) clinical outcomes from a highly-complex...
PDF
Analysis of viral strand sequence data and viral replication capacity could potentially lead to biological...
PDF
An important problem in epidemiology and medical research is the estimation of the causal effect...
PDF
Marginal structural models (MSM) provide a powerful tool for estimating the causal effect of a...
PDF
We consider the inverse problem of estimating a survival distribution when the survival times are...
Link
We propose a class of estimators of the treatment effect on a dichotomous outcome among...
PDF
We propose a class of estimators of the treatment effect on a dichotomous outcome among...
PDF
The causal effect of a treatment on an outcome is generally mediated by several intermediate...
PDF
Cawley et al. (2004) have recently mapped the locations of binding sites for three transcription...
PDF
In van der Laan and Dudoit (2003) we propose and theoretically study a unified loss...
PDF
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...
PDF
The accompanying articles by Dudoit et al. (2003b) and van der Laan et al. (2003)...
PDF
The present article proposes two step-down multiple testing procedures for asymptotic control of the family-wise...
PDF
The present article proposes general single-step multiple testing procedures for controlling Type I error rates...
PDF
Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions,...
PDF
In Part I of this article we propose a general cross-validation criterian for selecting among...
PDF
In order to estimate the causal effect of treatments on an outcome of interest, one...
PDF
Over the last two decades, non-parametric and semi-parametric approaches that adapt well known techniques such...
PDF
We propose a unified strategy for estimator construction, selection, and performance assessment in the presence...
PDF
Marginal Structural Models (MSM) have been introduced by Robins (1998a) as a powerful tool for...
PDF
We define a general statistical framework for multiple hypothesis testing and show that the correct...
PDF
Consider estimation of causal parameters in a marginal structural model for the discrete intensity of...
PDF
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...
PDF
Estimators for the parameter of interest in semiparametric models often depend on a guessed model...
PDF
Likelihood-based cross-validation is a statistical tool for selecting a density estimate based on n i.i.d....
PDF
Risk estimation is an important statistical question for the purposes of selecting a good estimator...
PDF
Recurrent events models have lately received a lot of attention in the literature. The majority...
PDF
Robins' causal inference theory assumes existence of treatment specific counterfactual variables so that the observed...
PDF
Estimation for bivariate right censored data is a problem that has had much study over...
PDF
In biostatistics applications interest often focuses on the estimation of the distribution of a time-variable...
Link
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...
PDF
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...
PDF
In point treatment marginal structural models with treatment A, outcome Y and covariates W, causal...
PDF
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...
Link
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...
Link
Researchers working with survival data are by now adept at handling issues associated with incomplete...
PDF
Researchers working with survival data are by now adept at handling issues associated with incomplete...
PDF
We propose a bivariate survival function estimator for a general right censored data structure that...
PDF
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...
Link
A great deal of recent attention has focused on the estimation of survival distributions based...
Link
A great deal of recent attention has focused on the estimation of survival distributions based...
PDF
Current methods for analysis of gene expression data are mostly based on clustering and classification...
Link
We have previously described a statistical framework for using gene expression data from cDNA microarrays...
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We study nonparametric estimation with two types of data structures. In the first data structure...
Link
We study nonparametric estimation with two types of data structures. In the first data structure...
Link
Recent developments in microarray technology make it possible to capture the gene expression profiles for...
Link
In this paper we develop a locally efficient one-step estimator of a multivariate survival function...
Link
In biostatistical applications interest often focuses on the estimation of the distribution of time T...
Link
We study nonparametric estimation with two types of data structures. In the first data structure...
Link
We study nonparametric estimation with two types of data structures. In the first data structure...
Link
The California Partners' Study is an ongoing investigation of heterosexual HIV transmission in partners of...
Link
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...
Link
Zhao and Tsiatis (1997) consider the problem of estimation of the distribution of the quality...
Link
In many observational studies one is concerned with comparing treatment specific survival distributions in the...
Link
The Kaplan-Meier estimator of a survival function is well known to be asymp- totically efficient...
Link
The Kaplan-Meier estimator of a survival function is well known to be asymp- totically efficient...
Link
We study efficient nonparametric maximum likelihood estimation of the distribution of onset and lifetime associated...
Link
We study efficient nonparametric maximum likelihood estimation of the distribution of onset and lifetime associated...
Link
In many biostatistical applications one is concerned with estimating the distribution of a survival time...
Link
For many sources of survival data, there is a delay between the recording of vital...
Link
In biostatistical applications interest often focuses on the estimation of the distribution of a failure...
Link
In biostatistical applications interest often focuses on the estimation of the distribution of time T...
Link
In estimation of a survival function, current status data arises when the only information available...
Link
In estimation of a survival function, current status data arises when the only information available...
Link
Randomly left or right truncated observations occur when one is concerned with estimation of the...
Link
In biostatistical applications interest is often focused on the estimation of the distribution of time...
Link
In estimation of a survival function, current status data arises when the only information available...
Survival Analysis
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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...
PDF
Optimal designs of dose levels in order to estimate parameters from a model for binary...
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We consider random design nonparametric regression when the response variable is subject to right censoring....
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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...
PDF
Providing information about the risk of disease and clinical factors that may increase or...
PDF
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....
PDF
The Bioconductor R package multtest implements widely applicable resampling-based single-step and stepwise multiple testing procedures...
PDF
Optimal designs of dose levels in order to estimate parameters from a model for binary...
Link
Optimal designs of dose levels in order to estimate parameters from a model for binary...
PDF
We propose a new method for predicting censored (and non-censored) clinical outcomes from a highly-complex...
PDF
We consider the inverse problem of estimating a survival distribution when the survival times are...
PDF
In van der Laan and Dudoit (2003) we propose and theoretically study a unified loss...
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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...
PDF
Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions,...
PDF
In Part I of this article we propose a general cross-validation criterian for selecting among...
PDF
Over the last two decades, non-parametric and semi-parametric approaches that adapt well known techniques such...
PDF
We propose a unified strategy for estimator construction, selection, and performance assessment in the presence...
PDF
Consider estimation of causal parameters in a marginal structural model for the discrete intensity of...
PDF
Estimators for the parameter of interest in semiparametric models often depend on a guessed model...
PDF
Recurrent events models have lately received a lot of attention in the literature. The majority...
PDF
Estimation for bivariate right censored data is a problem that has had much study over...
PDF
In biostatistics applications interest often focuses on the estimation of the distribution of a time-variable...
Link
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...
PDF
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...
PDF
In point treatment marginal structural models with treatment A, outcome Y and covariates W, causal...
PDF
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...
Link
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...
Link
Researchers working with survival data are by now adept at handling issues associated with incomplete...
PDF
Researchers working with survival data are by now adept at handling issues associated with incomplete...
PDF
We propose a bivariate survival function estimator for a general right censored data structure that...
Link
A great deal of recent attention has focused on the estimation of survival distributions based...
Link
A great deal of recent attention has focused on the estimation of survival distributions based...
Link
We study nonparametric estimation with two types of data structures. In the first data structure...
Link
We study nonparametric estimation with two types of data structures. In the first data structure...
Link
In this paper we develop a locally efficient one-step estimator of a multivariate survival function...
Link
In biostatistical applications interest often focuses on the estimation of the distribution of time T...
Link
We study nonparametric estimation with two types of data structures. In the first data structure...
Link
We study nonparametric estimation with two types of data structures. In the first data structure...
Link
Consider k equal size treatment groups and let the outcome of interest be a survival...
Link
Zhao and Tsiatis (1997) consider the problem of estimation of the distribution of the quality...
Link
In many observational studies one is concerned with comparing treatment specific survival distributions in the...
Link
We study efficient nonparametric maximum likelihood estimation of the distribution of onset and lifetime associated...
Link
In many biostatistical applications one is concerned with estimating the distribution of a survival time...
Link
For many sources of survival data, there is a delay between the recording of vital...
Link
In biostatistical applications interest often focuses on the estimation of the distribution of a failure...
Link
In estimation of a survival function, current status data arises when the only information available...
Link
In estimation of a survival function, current status data arises when the only information available...
Link
In biostatistical applications interest is often focused on the estimation of the distribution of time...
Link
In estimation of a survival function, current status data arises when the only information available...
No subject area
Link
Estimation for bivariate right censored data is a problem that has had much study over...
Link
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...
Link
Estimation of the number of mixture components (k) is an unsolved problem. Available methods for...
Link
In many applications the observed data can be viewed as a censored high dimensional full...
Link
In biostatistical applications interest often focuses on the estimation of the distribution of a time-until-event...
Link
In biostatistical applications, interest often focuses on the estimation of the distribution of time T...
Link
In this paper, the NPMLE in the one-dimensional line segment problem is defined and studied,...
Link
We consider nonparametric missing data models for which the censoring mechanism satisfies coarsening at random...
Link
In biostatistical applications interest is often focused on the estimation of the distribution of time...
Link
A large number of proposals for estimating the bivariate survival function under random censoring has...
Link
In estimation of a survival function, current status data arises when the only information available...
Link
A method is given for proving efficiency of NPMLE directly linked to empirical process theory....