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Articles 1 - 25 of 25
Full-Text Articles in Entire DC Network
A Likelihood Based Method For Real Time Estimation Of The Serial Interval And Reproductive Number Of An Epidemic, Laura Forsberg White, Marcello Pagano
A Likelihood Based Method For Real Time Estimation Of The Serial Interval And Reproductive Number Of An Epidemic, Laura Forsberg White, Marcello Pagano
Harvard University Biostatistics Working Paper Series
No abstract provided.
Spatio-Temporal Analysis Of Areal Data And Discovery Of Neighborhood Relationships In Conditionally Autoregressive Models, Subharup Guha, Louise Ryan
Spatio-Temporal Analysis Of Areal Data And Discovery Of Neighborhood Relationships In Conditionally Autoregressive Models, Subharup Guha, Louise Ryan
Harvard University Biostatistics Working Paper Series
No abstract provided.
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Harvard University Biostatistics Working Paper Series
No abstract provided.
Multiple Testing With An Empirical Alternative Hypothesis, James E. Signorovitch
Multiple Testing With An Empirical Alternative Hypothesis, James E. Signorovitch
Harvard University Biostatistics Working Paper Series
An optimal multiple testing procedure is identified for linear hypotheses under the general linear model, maximizing the expected number of false null hypotheses rejected at any significance level. The optimal procedure depends on the unknown data-generating distribution, but can be consistently estimated. Drawing information together across many hypotheses, the estimated optimal procedure provides an empirical alternative hypothesis by adapting to underlying patterns of departure from the null. Proposed multiple testing procedures based on the empirical alternative are evaluated through simulations and an application to gene expression microarray data. Compared to a standard multiple testing procedure, it is not unusual for …
Bayesian Hidden Markov Modeling Of Array Cgh Data, Subharup Guha, Yi Li, Donna Neuberg
Bayesian Hidden Markov Modeling Of Array Cgh Data, Subharup Guha, Yi Li, Donna Neuberg
Harvard University Biostatistics Working Paper Series
Genomic alterations have been linked to the development and progression of cancer. The technique of Comparative Genomic Hybridization (CGH) yields data consisting of fluorescence intensity ratios of test and reference DNA samples. The intensity ratios provide information about the number of copies in DNA. Practical issues such as the contamination of tumor cells in tissue specimens and normalization errors necessitate the use of statistics for learning about the genomic alterations from array-CGH data. As increasing amounts of array CGH data become available, there is a growing need for automated algorithms for characterizing genomic profiles. Specifically, there is a need for …
Spatial Cluster Detection For Censored Outcome Data, Andrea J. Cook, Diane Gold, Yi Li
Spatial Cluster Detection For Censored Outcome Data, Andrea J. Cook, Diane Gold, Yi Li
Harvard University Biostatistics Working Paper Series
No abstract provided.
Structural Inference In Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Xihong Lin, Donglin Zeng
Structural Inference In Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Xihong Lin, Donglin Zeng
Harvard University Biostatistics Working Paper Series
No abstract provided.
Estimation In Semiparametric Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Donglin Zeng, Xihong Lin
Estimation In Semiparametric Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Donglin Zeng, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Nonparametric Regression Using Local Kernel Estimating Equations For Correlated Failure Time Data, Zhangsheng Yu, Xihong Lin
Nonparametric Regression Using Local Kernel Estimating Equations For Correlated Failure Time Data, Zhangsheng Yu, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Causal Inference In Hybrid Intervention Trials Involving Treatment Choice, Qi Long, Rod Little, Xihong Lin
Causal Inference In Hybrid Intervention Trials Involving Treatment Choice, Qi Long, Rod Little, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Comparison Of Methods For Estimating The Causal Effect Of A Treatment In Randomized Clinical Trials Subject To Noncompliance, Rod Little, Qi Long, Xihong Lin
A Comparison Of Methods For Estimating The Causal Effect Of A Treatment In Randomized Clinical Trials Subject To Noncompliance, Rod Little, Qi Long, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Bayesian Smoothing Of Irregularly-Spaced Data Using Fourier Basis Functions, Christopher J. Paciorek
Bayesian Smoothing Of Irregularly-Spaced Data Using Fourier Basis Functions, Christopher J. Paciorek
Harvard University Biostatistics Working Paper Series
No abstract provided.
Predicting Future Responses Based On Possibly Misspecified Working Models, Tianxi Cai, Lu Tian, Scott D. Solomon, L.J. Wei
Predicting Future Responses Based On Possibly Misspecified Working Models, Tianxi Cai, Lu Tian, Scott D. Solomon, L.J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
An Informative Bayesian Structural Equation Model To Assess Source-Specific Health Effects Of Air Pollution, Margaret C. Nikolov, Brent A. Coull, Paul J. Catalano, John J. Godleski
An Informative Bayesian Structural Equation Model To Assess Source-Specific Health Effects Of Air Pollution, Margaret C. Nikolov, Brent A. Coull, Paul J. Catalano, John J. Godleski
Harvard University Biostatistics Working Paper Series
No abstract provided.
Mixed Multiplicative Factor Analysis Model For Air Pollution Exposure Assessment, Margaret C. Nikolov, Brent A. Coull, Paul J. Catalano, John J. Godleski
Mixed Multiplicative Factor Analysis Model For Air Pollution Exposure Assessment, Margaret C. Nikolov, Brent A. Coull, Paul J. Catalano, John J. Godleski
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Computationally Tractable Multivariate Random Effects Model For Clustered Binary Data, Brent A. Coull, E. Andres Houseman, Rebecca A. Betensky
A Computationally Tractable Multivariate Random Effects Model For Clustered Binary Data, Brent A. Coull, E. Andres Houseman, Rebecca A. Betensky
Harvard University Biostatistics Working Paper Series
No abstract provided.
Plasq: A Generalized Linear Model-Based Procedure To Determine Allelic Dosage Ini Cancer Cells From Snp Array Data, Thomas Laframboise, David P. Harrington, Barbara A. Weir
Plasq: A Generalized Linear Model-Based Procedure To Determine Allelic Dosage Ini Cancer Cells From Snp Array Data, Thomas Laframboise, David P. Harrington, Barbara A. Weir
Harvard University Biostatistics Working Paper Series
No abstract provided.
Using Profile Likelihood For Semiparametric Model Selection With Application To Proportional Hazards Mixed Models, Ronghui Xu, Anthony Gamst, Michael Donohue, Florin Vaida, David P. Harrington
Using Profile Likelihood For Semiparametric Model Selection With Application To Proportional Hazards Mixed Models, Ronghui Xu, Anthony Gamst, Michael Donohue, Florin Vaida, David P. Harrington
Harvard University Biostatistics Working Paper Series
No abstract provided.
Posterior Simulation In The Generalized Linear Model With Semiparmetric Random Effects, Subharup Guha
Posterior Simulation In The Generalized Linear Model With Semiparmetric Random Effects, Subharup Guha
Harvard University Biostatistics Working Paper Series
Generalized linear mixed models with semiparametric random effects are useful in a wide variety of Bayesian applications. When the random effects arise from a mixture of Dirichlet process (MDP) model, normal base measures and Gibbs sampling procedures based on the Pólya urn scheme are often used to simulate posterior draws. These algorithms are applicable in the conjugate case when (for a normal base measure) the likelihood is normal. In the non-conjugate case, the algorithms proposed by MacEachern and Müller (1998) and Neal (2000) are often applied to generate posterior samples. Some common problems associated with simulation algorithms for non-conjugate MDP …
Semiparametric Latent Variable Regression Models For Spatio-Temporal Modeling Of Mobile Source Particles In The Greater Boston Area, Alexandros Gryparis, Brent A. Coull, Joel Schwartz, Helen H. Suh
Semiparametric Latent Variable Regression Models For Spatio-Temporal Modeling Of Mobile Source Particles In The Greater Boston Area, Alexandros Gryparis, Brent A. Coull, Joel Schwartz, Helen H. Suh
Harvard University Biostatistics Working Paper Series
Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic …
Survival Analysis With Change Point Hazard Functions, Melody S. Goodman, Yi Li, Ram C. Tiwari
Survival Analysis With Change Point Hazard Functions, Melody S. Goodman, Yi Li, Ram C. Tiwari
Harvard University Biostatistics Working Paper Series
No abstract provided.
Selecting 'Significant' Differentially Expressed Genes From The Combined Perspective Of The Null And The Alternative, Beatrijs Moerkerke, Els Goetghebeur
Selecting 'Significant' Differentially Expressed Genes From The Combined Perspective Of The Null And The Alternative, Beatrijs Moerkerke, Els Goetghebeur
Harvard University Biostatistics Working Paper Series
No abstract provided.
Evaluating Prediction Rules For T-Year Survivors With Censored Regression Models, Hajime Uno, Tianxi Cai, Lu Tian, L.J. Wei
Evaluating Prediction Rules For T-Year Survivors With Censored Regression Models, Hajime Uno, Tianxi Cai, Lu Tian, L.J. Wei
Harvard University Biostatistics Working Paper Series
Suppose that we are interested in establishing simple, but reliable rules for predicting future t-year survivors via censored regression models. In this article, we present inference procedures for evaluating such binary classification rules based on various prediction precision measures quantified by the overall misclassification rate, sensitivity and specificity, and positive and negative predictive values. Specifically, under various working models we derive consistent estimators for the above measures via substitution and cross validation estimation procedures. Furthermore, we provide large sample approximations to the distributions of these nonsmooth estimators without assuming that the working model is correctly specified. Confidence intervals, for example, …
A Diagnostic Test For The Mixing Distribution In A Generalised Linear Mixed Model, Eric J. Tchetgen, Brent A. Coull
A Diagnostic Test For The Mixing Distribution In A Generalised Linear Mixed Model, Eric J. Tchetgen, Brent A. Coull
Harvard University Biostatistics Working Paper Series
We introduce a diagnostic test for the mixing distribution in a generalised linear mixed model. The test is based on the difference between the marginal maximum likelihood and conditional maximum likelihood estimates of a subset of the fixed effects in the model. We derive the asymptotic variance of this difference, and propose a test statistic that has a limiting chi-square distribution under the null hypothesis that the mixing distribution is correctly specified. For the important special case of the logistic regression model with random intercepts, we evaluate via simulation the power of the test in finite samples under several alternative …
Regression Analysis For The Partial Area Under The Roc Curve, Tianxi Cai, Lori E. Dodd
Regression Analysis For The Partial Area Under The Roc Curve, Tianxi Cai, Lori E. Dodd
Harvard University Biostatistics Working Paper Series
No abstract provided.