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Another Friday Test, Sid Marchseventeen 2017 Selected Works

Another Friday Test, Sid Marchseventeen

Sid Marchseventeen

Donec a gravida lorem. Fusce euismod, lectus sed gravida lacinia, nisi neque dictum felis, ac iaculis eros lacus ac dolor. Etiam feugiat est non leo pharetra, ac ultrices ligula dapibus. Pellentesque vestibulum sed diam eget suscipit. Aliquam scelerisque ut erat in dignissim. Nullam purus massa, hendrerit ut auctor ut, vehicula et tellus. Suspendisse quis lobortis ligula. Ut malesuada lorem in vulputate volutpat. Duis ac nisi id turpis finibus ullamcorper nec in enim. Donec auctor lacinia tempor. Aenean sit amet orci elit.


Test Demo Search - Two, Sid Threemarchsixteen 2017 Selected Works

Test Demo Search - Two, Sid Threemarchsixteen

Sid Threemarchsixteen

Quisque convallis porta dictum. Donec nulla mauris, scelerisque a suscipit at, hendrerit quis lorem. Nulla quis pharetra libero. Donec et quam leo. Nullam pulvinar vitae augue ut facilisis. In vitae mauris non sem sodales fringilla et et lacus. Cras tincidunt, leo sit amet elementum condimentum, odio erat malesuada lectus, ut ornare eros erat ut erat. Maecenas pretium semper quam quis fringilla. Praesent faucibus blandit risus, sit amet hendrerit nisi euismod a. Duis nec bibendum ligula. Duis volutpat placerat tellus, sit amet accumsan magna ullamcorper a. Suspendisse augue orci, accumsan ornare porta non, bibendum vel odio. Revised.


Test Demo Search - One, Sid Threemarchsixteen 2017 Selected Works

Test Demo Search - One, Sid Threemarchsixteen

Sid Threemarchsixteen

Donec a ultrices justo, vestibulum commodo ante. Praesent lectus velit, lacinia et odio ut, fringilla eleifend quam. Sed sit amet vestibulum nulla. Cras vestibulum pulvinar ligula, vitae cursus mi bibendum volutpat. Aliquam nunc libero, lobortis quis diam vel, fermentum efficitur turpis. Vestibulum eget porttitor nisl, a condimentum ex. Quisque ut condimentum sapien. Donec vehicula faucibus vulputate. Vivamus massa dolor, lacinia id laoreet id, interdum at erat. Revised again.


Novel Computational Methods For Censored Data And Regression, Yifan Yang 2017 University of Kentucky

Novel Computational Methods For Censored Data And Regression, Yifan Yang

Theses and Dissertations--Statistics

This dissertation can be divided into three topics. In the first topic, we derived a recursive algorithm for the constrained Kaplan-Meier estimator, which promotes the computation speed up to fifty times compared to the current method that uses EM algorithm. We also showed how this leads to the vast improvement of empirical likelihood analysis with right censored data. After a brief review of regularized regressions, we investigated the computational problems in the parametric/non-parametric hybrid accelerated failure time models and its regularization in a high dimensional setting. We also illustrated that, when the number of pieces increases, the discussed models ...


Survival Analysis In A Clinical Setting, Yunzhao Liu 2016 Washington University in St. Louis

Survival Analysis In A Clinical Setting, Yunzhao Liu

Arts & Sciences Electronic Theses and Dissertations

With the fast paced advancement of modern medicine, cancer treatments have improved greatly over the past few decades; however, the overall survival rate has not improved for head neck squamous cell carcinoma (HNSCC). Traditionally, the general affected population of HNSCC was male over 50-60 years of age, whom have had history of alcohol and tobacco use. Conversely, in the recent decades, HNSCC has exhibited significant rise in younger patients, largely due to the increase in human papillomavirus (HPV) infection among young adults.

Generally, HPV as the most prevalent sexually transmitted disease, consisted of strains that do not cause harm to ...


Joint Modelling In Liver Transplantation, Elizabeth M. Renouf 2016 The University of Western Ontario

Joint Modelling In Liver Transplantation, Elizabeth M. Renouf

Electronic Thesis and Dissertation Repository

In the setting of liver transplantation, clinical trials and transplant registries regularly collect repeated measurements of clinical biomarkers which may be strongly associated with a time-to-event such as graft failure or disease recurrence. Multiple time-to-event outcomes are routinely collected. However, joint models are rarely used. This thesis will describe important considerations for joint modelling in the setting of liver transplantation. We will focus on transplant registry data from the United States. We develop a new tool for joint modelling in the context where a critical health event can be tracked in the longitudinal biomarker and often presents as a non-linear ...


Another Coauthor Test Here, Sid Twentythree, Sid B. Testeroni 2016 bepress university

Another Coauthor Test Here, Sid Twentythree, Sid B. Testeroni

Sid Testeroni

It's a TEST.


Conditional Screening For Ultra-High Dimensional Covariates With Survival Outcomes, Hyokyoung Grace Hong, Jian Kang, Yi Li 2016 Michigan State University

Conditional Screening For Ultra-High Dimensional Covariates With Survival Outcomes, Hyokyoung Grace Hong, Jian Kang, Yi Li

The University of Michigan Department of Biostatistics Working Paper Series

Identifying important biomarkers that are predictive for cancer patients' prognosis is key in gaining better insights into the biological influences on the disease and has become a critical component of precision medicine. The emergence of large-scale biomedical survival studies, which typically involve excessive number of biomarkers, has brought high demand in designing efficient screening tools for selecting predictive biomarkers. The vast amount of biomarkers defies any existing variable selection methods via regularization. The recently developed variable screening methods, though powerful in many practical setting, fail to incorporate prior information on the importance of each biomarker and are less powerful in ...


Friday Test - One, Sid Threemarchsixteen 2016 Selected Works

Friday Test - One, Sid Threemarchsixteen

Sid Threemarchsixteen

Curabitur a tortor ut erat mattis scelerisque. Fusce blandit nec risus ut venenatis. Nam ac aliquet turpis, ut cursus orci. Nulla hendrerit diam eget mauris hendrerit, non mattis ante feugiat. Suspendisse potenti. Fusce pharetra nec arcu sit amet commodo. Nullam facilisis risus non quam rutrum sodales. Vivamus ac finibus magna. Vestibulum ut mi gravida, gravida enim sit amet, accumsan augue. Sed vitae tortor porta nulla tristique porttitor. Praesent eleifend sem ut diam gravida, sit amet ornare massa elementum. Cras ullamcorper risus lorem, at imperdiet ante pellentesque non. Integer nec augue nisi.


Models For Hsv Shedding Must Account For Two Levels Of Overdispersion, Amalia Magaret 2016 University of Washington - Seattle Campus

Models For Hsv Shedding Must Account For Two Levels Of Overdispersion, Amalia Magaret

UW Biostatistics Working Paper Series

We have frequently implemented crossover studies to evaluate new therapeutic interventions for genital herpes simplex virus infection. The outcome measured to assess the efficacy of interventions on herpes disease severity is the viral shedding rate, defined as the frequency of detection of HSV on the genital skin and mucosa. We performed a simulation study to ascertain whether our standard model, which we have used previously, was appropriately considering all the necessary features of the shedding data to provide correct inference. We simulated shedding data under our standard, validated assumptions and assessed the ability of 5 different models to reproduce the ...


Empirical Likelihood And Differentiable Functionals, Zhiyuan Shen 2016 University of Kentucky

Empirical Likelihood And Differentiable Functionals, Zhiyuan Shen

Theses and Dissertations--Statistics

Empirical likelihood (EL) is a recently developed nonparametric method of statistical inference. It has been shown by Owen (1988,1990) and many others that empirical likelihood ratio (ELR) method can be used to produce nice confidence intervals or regions. Owen (1988) shows that -2logELR converges to a chi-square distribution with one degree of freedom subject to a linear statistical functional in terms of distribution functions. However, a generalization of Owen's result to the right censored data setting is difficult since no explicit maximization can be obtained under constraint in terms of distribution functions. Pan and Zhou (2002), instead ...


Statistical Methods For Environmental Exposure Data Subject To Detection Limits, Yuchen Yang 2016 University of Kentucky

Statistical Methods For Environmental Exposure Data Subject To Detection Limits, Yuchen Yang

Theses and Dissertations--Statistics

In this dissertation, we develop unified and efficient nonparametric statistical methods for estimating and comparing environmental exposure distributions in presence of detection limits. In the first part, we propose a kernel-smoothed nonparametric estimator for the exposure distribution without imposing any independence assumption between the exposure level and detection limit. We show that the proposed estimator is consistent and asymptotically normal. Simulation studies demonstrate that the proposed estimator performs well in practical situations. A colon cancer study is provided for illustration. In the second part, we develop a class of test statistics to compare exposure distributions between two groups by using ...


Introduction To The Analysis Of Survival Data In The Presence Of Competing Risks, Peter Austin, Douglas Lee, Jason Fine 2015 Institute for Clinical Evaluative Sciences

Introduction To The Analysis Of Survival Data In The Presence Of Competing Risks, Peter Austin, Douglas Lee, Jason Fine

Peter Austin

Competing risks occur frequently in the analysis of survival data. A competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. In a study examining time to death attributable to cardiovascular causes, death attributable to noncardiovascular causes is a competing risk. When estimating the crude incidence of outcomes, analysts should use the cumulative incidence function, rather than the complement of the Kaplan-Meier survival function. The use of the Kaplan-Meier survival function results in estimates of incidence that are biased upward, regardless of whether the competing events are independent of one another. When fitting regression ...


Doubly Robust And Efficient Estimation Of Marginal Structural Models For The Hazard Function, Wenjing Zheng, Maya Petersen, Mark van der Laan 2015 University of California, Berkeley

Doubly Robust And Efficient Estimation Of Marginal Structural Models For The Hazard Function, Wenjing Zheng, Maya Petersen, Mark Van Der Laan

Wenjing Zheng

In social and health sciences, many research questions involve understanding the causal effect of a longitudinal treatment on mortality (or time-to-event outcomes in general). Often, treatment status may change in response to past covariates that are risk factors for mortality, and in turn, treatment status may also affect such subsequent covariates. In these situations, Marginal Structural Models (MSMs), introduced by Robins (1997), are well-established and widely used tools to account for time-varying confounding. In particular, a MSM can be used to specify the intervention-specific counterfactual hazard function, i.e. the hazard for the outcome of a subject in an ideal ...


Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. van der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng 2015 Division of Biostatistics, School of Public Health, University of California, Berkeley

Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. Van Der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng

Mark J. van der Laan

Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions, with typically unknown and intricate correlation patterns among variables. Addressing these inference questions satisfactorily requires: (i) an intensive and thorough search of the parameter space to generate good candidate estimators, (ii) an approach for selecting an optimal estimator among these candidates, and (iii) a method for reliably assessing the performance of the resulting estimator. We propose a unified loss-based methodology for estimator construction, selection, and performance assessment with cross-validation. In this approach, the parameter of interest is defined as the risk minimizer for a suitable ...


Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. van der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng 2015 Division of Biostatistics, School of Public Health, University of California, Berkeley

Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. Van Der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng

Mark J. van der Laan

Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions, with typically unknown and intricate correlation patterns among variables. Addressing these inference questions satisfactorily requires: (i) an intensive and thorough search of the parameter space to generate good candidate estimators, (ii) an approach for selecting an optimal estimator among these candidates, and (iii) a method for reliably assessing the performance of the resulting estimator. We propose a unified loss-based methodology for estimator construction, selection, and performance assessment with cross-validation. In this approach, the parameter of interest is defined as the risk minimizer for a suitable ...


Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. van der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng 2015 Division of Biostatistics, School of Public Health, University of California, Berkeley

Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. Van Der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng

Mark J. van der Laan

Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions, with typically unknown and intricate correlation patterns among variables. Addressing these inference questions satisfactorily requires: (i) an intensive and thorough search of the parameter space to generate good candidate estimators, (ii) an approach for selecting an optimal estimator among these candidates, and (iii) a method for reliably assessing the performance of the resulting estimator. We propose a unified loss-based methodology for estimator construction, selection, and performance assessment with cross-validation. In this approach, the parameter of interest is defined as the risk minimizer for a suitable ...


Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. van der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng 2015 Division of Biostatistics, School of Public Health, University of California, Berkeley

Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. Van Der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng

Mark J. van der Laan

Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions, with typically unknown and intricate correlation patterns among variables. Addressing these inference questions satisfactorily requires: (i) an intensive and thorough search of the parameter space to generate good candidate estimators, (ii) an approach for selecting an optimal estimator among these candidates, and (iii) a method for reliably assessing the performance of the resulting estimator. We propose a unified loss-based methodology for estimator construction, selection, and performance assessment with cross-validation. In this approach, the parameter of interest is defined as the risk minimizer for a suitable ...


Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. van der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng 2015 Division of Biostatistics, School of Public Health, University of California, Berkeley

Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. Van Der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng

Mark J. van der Laan

Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions, with typically unknown and intricate correlation patterns among variables. Addressing these inference questions satisfactorily requires: (i) an intensive and thorough search of the parameter space to generate good candidate estimators, (ii) an approach for selecting an optimal estimator among these candidates, and (iii) a method for reliably assessing the performance of the resulting estimator. We propose a unified loss-based methodology for estimator construction, selection, and performance assessment with cross-validation. In this approach, the parameter of interest is defined as the risk minimizer for a suitable ...


Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. van der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng 2015 Division of Biostatistics, School of Public Health, University of California, Berkeley

Loss-Based Estimation With Cross-Validation: Applications To Microarray Data Analysis And Motif Finding, Sandrine Dudoit, Mark J. Van Der Laan, Sunduz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng

Mark J. van der Laan

Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions, with typically unknown and intricate correlation patterns among variables. Addressing these inference questions satisfactorily requires: (i) an intensive and thorough search of the parameter space to generate good candidate estimators, (ii) an approach for selecting an optimal estimator among these candidates, and (iii) a method for reliably assessing the performance of the resulting estimator. We propose a unified loss-based methodology for estimator construction, selection, and performance assessment with cross-validation. In this approach, the parameter of interest is defined as the risk minimizer for a suitable ...


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