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cornell biostatistics and data science: Semiparametric Regression with R Jaroslaw Harezlak, David Ruppert, Matt P. Wand, 2018-12-12 This easy-to-follow applied book on semiparametric regression methods using R is intended to close the gap between the available methodology and its use in practice. Semiparametric regression has a large literature but much of it is geared towards data analysts who have advanced knowledge of statistical methods. While R now has a great deal of semiparametric regression functionality, many of these developments have not trickled down to rank-and-file statistical analysts. The authors assemble a broad range of semiparametric regression R analyses and put them in a form that is useful for applied researchers. There are chapters devoted to penalized spines, generalized additive models, grouped data, bivariate extensions of penalized spines, and spatial semi-parametric regression models. Where feasible, the R code is provided in the text, however the book is also accompanied by an external website complete with datasets and R code. Because of its flexibility, semiparametric regression has proven to be of great value with many applications in fields as diverse as astronomy, biology, medicine, economics, and finance. This book is intended for applied statistical analysts who have some familiarity with R. |
cornell biostatistics and data science: Statistics and Data Analysis for Financial Engineering David Ruppert, David S. Matteson, 2015-04-21 The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest. |
cornell biostatistics and data science: Introduction to Data Science Laura Igual, Santi Seguí, 2017-02-22 This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website. |
cornell biostatistics and data science: Transformation and Weighting in Regression Raymond J. Carroll, David Ruppert, 2017-10-19 This monograph provides a careful review of the major statistical techniques used to analyze regression data with nonconstant variability and skewness. The authors have developed statistical techniques--such as formal fitting methods and less formal graphical techniques-- that can be applied to many problems across a range of disciplines, including pharmacokinetics, econometrics, biochemical assays, and fisheries research. While the main focus of the book in on data transformation and weighting, it also draws upon ideas from diverse fields such as influence diagnostics, robustness, bootstrapping, nonparametric data smoothing, quasi-likelihood methods, errors-in-variables, and random coefficients. The authors discuss the computation of estimates and give numerous examples using real data. The book also includes an extensive treatment of estimating variance functions in regression. |
cornell biostatistics and data science: Fundamentals of Biostatistics Bernard Rosner, 2015-07-29 Bernard Rosner's FUNDAMENTALS OF BIOSTATISTICS is a practical introduction to the methods, techniques, and computation of statistics with human subjects. It prepares students for their future courses and careers by introducing the statistical methods most often used in medical literature. Rosner minimizes the amount of mathematical formulation (algebra-based) while still giving complete explanations of all the important concepts. As in previous editions, a major strength of this book is that every new concept is developed systematically through completely worked out examples from current medical research problems. Most methods are illustrated with specific instructions as to implementation using software either from SAS, Stata, R, Excel or Minitab. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version. |
cornell biostatistics and data science: Patient-Reported Outcomes Joseph C. Cappelleri, Kelly H. Zou, Andrew G. Bushmakin, Jose Ma. J. Alvir, Demissie Alemayehu, Tara Symonds, 2013-12-20 Advancing the development, validation, and use of patient-reported outcome (PRO) measures, Patient-Reported Outcomes: Measurement, Implementation and Interpretation helps readers develop and enrich their understanding of PRO methodology, particularly from a quantitative perspective. Designed for biopharmaceutical researchers and others in the health sciences community, it provides an up-to-date volume on conceptual and analytical issues of PRO measures. The book discusses key concepts relating to the measurement, implementation, and interpretation of PRO measures. It covers both introductory and advanced psychometric and biostatistical methods for constructing and analyzing PRO measures. The authors include many relevant real-life applications based on their extensive first-hand experiences in the pharmaceutical industry. They implement a wealth of simulated datasets to illustrate concepts and heighten understanding based on practical scenarios. For readers interested in conducting statistical analyses of PRO measures and delving more deeply into the analytic details, most chapters contain SAS code and output that illustrate the methodology. Along with providing numerous references, the book highlights current regulatory guidelines. |
cornell biostatistics and data science: Statistical Topics in Health Economics and Outcomes Research Demissie Alemayehu, PhD, Joseph C. Cappelleri, PhD, Birol Emir, PhD, Kelly H. Zou, PhD, Pstat, 2017-11-22 With ever-rising healthcare costs, evidence generation through Health Economics and Outcomes Research (HEOR) plays an increasingly important role in decision-making about the allocation of resources. Accordingly, it is now customary for health technology assessment and reimbursement agencies to request for HEOR evidence, in addition to data from clinical trials, to inform decisions about patient access to new treatment options. While there is a great deal of literature on HEOR, there is a need for a volume that presents a coherent and unified review of the major issues that arise in application, especially from a statistical perspective. Statistical Topics in Health Economics and Outcomes Research fulfils that need by presenting an overview of the key analytical issues and best practice. Special attention is paid to key assumptions and other salient features of statistical methods customarily used in the area, and appropriate and relatively comprehensive references are made to emerging trends. The content of the book is purposefully designed to be accessible to readers with basic quantitative backgrounds, while providing an in-depth coverage of relatively complex statistical issues. The book will make a very useful reference for researchers in the pharmaceutical industry, academia, and research institutions involved with HEOR studies. The targeted readers may include statisticians, data scientists, epidemiologists, outcomes researchers, health economists, and healthcare policy and decision-makers. |
cornell biostatistics and data science: Introduction to Biostatistics Ronald N. Forthofer, Eun Sul Lee, 2014-05-19 The Biostatistics course is often found in the schools of public Health, medical schools, and, occasionally, in statistics and biology departments. The population of students in these courses is a diverse one, with varying preparedness. Introduction to Biostatistics assumes the reader has at least two years of high school algebra, but no previous exposure to statistics is required. Written for individuals who might be fearful of mathematics, this book minimizes the technical difficulties and emphasizes the importance of statistics in scientific investigation. An understanding of underlying design and analysis is stressed. The limitations of the research, design and analytical techniques are discussed, allowing the reader to accurately interpret results. Real data, both processed and raw, are used extensively in examples and exercises. Statistical computing packages - MINITAB, SAS and Stata - are integrated. The use of the computer and software allows a sharper focus on the concepts, letting the computer do the necessary number-crunching. - Emphasizes underlying statistical concepts more than competing texts - Focuses on experimental design and analysis, at an elementary level - Includes an introduction to linear correlation and regression - Statistics are central: probability is downplayed - Presents life tables and survival analysis - Appendix with solutions to many exercises - Special instructor's manual with solution to all exercises |
cornell biostatistics and data science: Spectral Methods for Data Science Yuxin Chen, 2021 This monograph presents a systematic, yet accessible introduction to spectral methods from a modern statistical perspective. It is essential reading for all students, researchers and practitioners working in Data Science. |
cornell biostatistics and data science: Introduction to Biostatistics with JMP Steve Figard, 2019-10-04 Explore biostatistics using JMP® in this refreshing introduction Presented in an easy-to-understand way, Introduction to Biostatistics with JMP® introduces undergraduate students in the biological sciences to the most commonly used (and misused) statistical methods that they will need to analyze their experimental data using JMP. It covers many of the basic topics in statistics using biological examples for exercises so that the student biologists can see the relevance to future work in the problems addressed. The book starts by teaching students how to become confident in executing the right analysis by thinking like a statistician then moves into the application of specific tests. Using the powerful capabilities of JMP, the book addresses problems requiring analysis by chi-square tests, t tests, ANOVA analysis, various regression models, DOE, and survival analysis. Topics of particular interest to the biological or health science field include odds ratios, relative risk, and survival analysis. The author uses an engaging, conversational tone to explain concepts and keep readers interested in learning more. The book aims to create bioscientists who can competently incorporate statistics into their investigative toolkits to solve biological research questions as they arise. |
cornell biostatistics and data science: Strength in Numbers: The Rising of Academic Statistics Departments in the U. S. Alan Agresti, Xiao-Li Meng, 2012-11-02 Statistical science as organized in formal academic departments is relatively new. With a few exceptions, most Statistics and Biostatistics departments have been created within the past 60 years. This book consists of a set of memoirs, one for each department in the U.S. created by the mid-1960s. The memoirs describe key aspects of the department’s history -- its founding, its growth, key people in its development, success stories (such as major research accomplishments) and the occasional failure story, PhD graduates who have had a significant impact, its impact on statistical education, and a summary of where the department stands today and its vision for the future. Read here all about how departments such as at Berkeley, Chicago, Harvard, and Stanford started and how they got to where they are today. The book should also be of interests to scholars in the field of disciplinary history. |
cornell biostatistics and data science: Handbook of Environmental and Ecological Statistics Alan E. Gelfand, Montserrat Fuentes, Jennifer A. Hoeting, Richard Lyttleton Smith, 2019-01-15 This handbook focuses on the enormous literature applying statistical methodology and modelling to environmental and ecological processes. The 21st century statistics community has become increasingly interdisciplinary, bringing a large collection of modern tools to all areas of application in environmental processes. In addition, the environmental community has substantially increased its scope of data collection including observational data, satellite-derived data, and computer model output. The resultant impact in this latter community has been substantial; no longer are simple regression and analysis of variance methods adequate. The contribution of this handbook is to assemble a state-of-the-art view of this interface. Features: An internationally regarded editorial team. A distinguished collection of contributors. A thoroughly contemporary treatment of a substantial interdisciplinary interface. Written to engage both statisticians as well as quantitative environmental researchers. 34 chapters covering methodology, ecological processes, environmental exposure, and statistical methods in climate science. |
cornell biostatistics and data science: Semiparametric Regression David Ruppert, M. P. Wand, R. J. Carroll, 2003-07-14 Semiparametric regression is concerned with the flexible incorporation of non-linear functional relationships in regression analyses. Any application area that benefits from regression analysis can also benefit from semiparametric regression. Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular fashion. The authors make liberal use of graphics and examples plus case studies taken from environmental, financial, and other applications. They include practical advice on implementation and pointers to relevant software. The 2003 book is suitable as a textbook for students with little background in regression as well as a reference book for statistically oriented scientists such as biostatisticians, econometricians, quantitative social scientists, epidemiologists, with a good working knowledge of regression and the desire to begin using more flexible semiparametric models. Even experts on semiparametric regression should find something new here. |
cornell biostatistics and data science: Modern Statistics for Modern Biology SUSAN. HUBER HOLMES (WOLFGANG.), Wolfgang Huber, 2018 |
cornell biostatistics and data science: An Introduction to Data Science Jeffrey S. Saltz, Jeffrey M. Stanton, 2017-08-25 An Introduction to Data Science is an easy-to-read data science textbook for those with no prior coding knowledge. It features exercises at the end of each chapter, author-generated tables and visualizations, and R code examples throughout. |
cornell biostatistics and data science: A Guide to the Scientific Career Mohammadali M. Shoja, Anastasia Arynchyna, Marios Loukas, Anthony V. D'Antoni, Sandra M. Buerger, Marion Karl, R. Shane Tubbs, 2020-01-09 A concise, easy-to-read source of essential tips and skills for writing research papers and career management In order to be truly successful in the biomedical professions, one must have excellent communication skills and networking abilities. Of equal importance is the possession of sufficient clinical knowledge, as well as a proficiency in conducting research and writing scientific papers. This unique and important book provides medical students and residents with the most commonly encountered topics in the academic and professional lifestyle, teaching them all of the practical nuances that are often only learned through experience. Written by a team of experienced professionals to help guide younger researchers, A Guide to the Scientific Career: Virtues, Communication, Research and Academic Writing features ten sections composed of seventy-four chapters that cover: qualities of research scientists; career satisfaction and its determinants; publishing in academic medicine; assessing a researcher’s scientific productivity and scholarly impact; manners in academics; communication skills; essence of collaborative research; dealing with manipulative people; writing and scientific misconduct: ethical and legal aspects; plagiarism; research regulations, proposals, grants, and practice; publication and resources; tips on writing every type of paper and report; and much more. An easy-to-read source of essential tips and skills for scientific research Emphasizes good communication skills, sound clinical judgment, knowledge of research methodology, and good writing skills Offers comprehensive guidelines that address every aspect of the medical student/resident academic and professional lifestyle Combines elements of a career-management guide and publication guide in one comprehensive reference source Includes selected personal stories by great researchers, fascinating writers, inspiring mentors, and extraordinary clinicians/scientists A Guide to the Scientific Career: Virtues, Communication, Research and Academic Writing is an excellent interdisciplinary text that will appeal to all medical students and scientists who seek to improve their writing and communication skills in order to make the most of their chosen career. |
cornell biostatistics and data science: Fundamentals of High-Dimensional Statistics Johannes Lederer, 2021-11-16 This textbook provides a step-by-step introduction to the tools and principles of high-dimensional statistics. Each chapter is complemented by numerous exercises, many of them with detailed solutions, and computer labs in R that convey valuable practical insights. The book covers the theory and practice of high-dimensional linear regression, graphical models, and inference, ensuring readers have a smooth start in the field. It also offers suggestions for further reading. Given its scope, the textbook is intended for beginning graduate and advanced undergraduate students in statistics, biostatistics, and bioinformatics, though it will be equally useful to a broader audience. |
cornell biostatistics and data science: The Basic Practice of Statistics David S. Moore, 2010 This is a clear and innovative overview of statistics which emphasises major ideas, essential skills and real-life data. The organisation and design has been improved for the fifth edition, coverage of engaging, real-world topics has been increased and content has been updated to appeal to today's trends and research. |
cornell biostatistics and data science: Statistical Design George Casella, 2008-04-03 Statistical design is one of the fundamentals of our subject, being at the core of the growth of statistics during the previous century. In this book the basic theoretical underpinnings are covered. It describes the principles that drive good designs and good statistics. Design played a key role in agricultural statistics and set down principles of good practice, principles that still apply today. Statistical design is all about understanding where the variance comes from, and making sure that is where the replication is. Indeed, it is probably correct to say that these principles are even more important today. |
cornell biostatistics and data science: Principles and Practice of Clinical Research John I. Gallin, Frederick P Ognibene, 2011-04-28 The second edition of this innovative work again provides a unique perspective on the clinical discovery process by providing input from experts within the NIH on the principles and practice of clinical research. Molecular medicine, genomics, and proteomics have opened vast opportunities for translation of basic science observations to the bedside through clinical research. As an introductory reference it gives clinical investigators in all fields an awareness of the tools required to ensure research protocols are well designed and comply with the rigorous regulatory requirements necessary to maximize the safety of research subjects. Complete with sections on the history of clinical research and ethics, copious figures and charts, and sample documents it serves as an excellent companion text for any course on clinical research and as a must-have reference for seasoned researchers.*Incorporates new chapters on Managing Conflicts of Interest in Human Subjects Research, Clinical Research from the Patient's Perspective, The Clinical Researcher and the Media, Data Management in Clinical Research, Evaluation of a Protocol Budget, Clinical Research from the Industry Perspective, and Genetics in Clinical Research *Addresses the vast opportunities for translation of basic science observations to the bedside through clinical research*Delves into data management and addresses how to collect data and use it for discovery*Contains valuable, up-to-date information on how to obtain funding from the federal government |
cornell biostatistics and data science: What is a P-value Anyway? Andrew Vickers, 2010 What is a p-value Anyway? offers a fun introduction to the fundamental principles of statistics, presenting the essential concepts in thirty-four brief, enjoyable stories. Drawing on his experience as a medical researcher, Vickers blends insightful explanations and humor, with minimal math, to help readers understand and interpret the statistics they read every day. Describing data; Data distributions; Variation of study results: confidence intervals; Hypothesis testing; Regression and decision making; Some common statistical errors, and what they teach us For all readers interested in statistics. |
cornell biostatistics and data science: The Practice of Statistics in the Life Sciences Brigitte Baldi, David S. Moore, 2013-12-15 This remarkably engaging textbook gives biology students an introduction to statistical practice all their own. It covers essential statistical topics with examples and exercises drawn from across the life sciences, including the fields of nursing, public health, and allied health. Based on David Moore’s The Basic Practice of Statistics, PSLS mirrors that #1 bestseller’s signature emphasis on statistical thinking, real data, and what statisticians actually do. The new edition includes new and updated exercises, examples, and samples of real data, as well as an expanded range of media tools for students and instructors. |
cornell biostatistics and data science: Statistical Methods in Molecular Biology Heejung Bang, Xi Kathy Zhou, Heather L. van Epps, Madhu Mazumdar, 2016-08-23 This progressive book presents the basic principles of proper statistical analyses. It progresses to more advanced statistical methods in response to rapidly developing technologies and methodologies in the field of molecular biology. |
cornell biostatistics and data science: An Introduction to Biostatistics Thomas Glover, Kevin Mitchell, 2015-06-29 For over a decade, Glover and Mitchell have provided life-sciences students with an accessible, complete introduction to the use of statistics in their disciplines. The authors emphasize the relationships between probability, probability distributions, and hypothesis testing using both parametric and nonparametric analyses. Copious examples throughout the text apply concepts and theories to real questions faced by researchers in biology, environmental science, biochemistry, and health sciences. Dozens of examples and problems are new to the Third Edition, as are “Concept Checks”—short questions that allow readers to immediately gauge their mastery of the topics presented. Regardless of mathematical background, all readers will appreciate the value of statistics as a fundamental quantitative skill for the life sciences. |
cornell biostatistics and data science: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning Segall, Richard S., Niu, Gao, 2022-01-07 During these uncertain and turbulent times, intelligent technologies including artificial neural networks (ANN) and machine learning (ML) have played an incredible role in being able to predict, analyze, and navigate unprecedented circumstances across a number of industries, ranging from healthcare to hospitality. Multi-factor prediction in particular has been especially helpful in dealing with the most current pressing issues such as COVID-19 prediction, pneumonia detection, cardiovascular diagnosis and disease management, automobile accident prediction, and vacation rental listing analysis. To date, there has not been much research content readily available in these areas, especially content written extensively from a user perspective. Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning is designed to cover a brief and focused range of essential topics in the field with perspectives, models, and first-hand experiences shared by prominent researchers, discussing applications of artificial neural networks (ANN) and machine learning (ML) for biomedical and business applications and a listing of current open-source software for neural networks, machine learning, and artificial intelligence. It also presents summaries of currently available open source software that utilize neural networks and machine learning. The book is ideal for professionals, researchers, students, and practitioners who want to more fully understand in a brief and concise format the realm and technologies of artificial neural networks (ANN) and machine learning (ML) and how they have been used for prediction of multi-disciplinary research problems in a multitude of disciplines. |
cornell biostatistics and data science: Computational Biomedicine Peter Coveney, Vanessa Díaz-Zuccarini, Peter Hunter, Marco Viceconti, 2014-06 Computational Biomedicine unifies the different strands of a broad-ranging subject to demonstrate the power of a tool that has the potential to revolutionise our understanding of the human body, and the therapeutic strategies available to maintain and protect it. |
cornell biostatistics and data science: Basic Biostatistics Gerstman, 2014-02-07 Basic Biostatistics is a concise, introductory text that covers biostatistical principles and focuses on the common types of data encountered in public health and biomedical fields. The text puts equal emphasis on exploratory and confirmatory statistical methods. Sampling, exploratory data analysis, estimation, hypothesis testing, and power and precision are covered through detailed, illustrative examples. The book is organized into three parts: Part I addresses basic concepts and techniques; Part II covers analytic techniques for quantitative response variables; and Part III covers techniques for categorical responses. The Second Edition offers many new exercises as well as an all new chapter on Poisson Random Variables and the Analysis of Rates. With language, examples, and exercises that are accessible to students with modest mathematical backgrounds, this is the perfect introductory biostatistics text for undergraduates and graduates in various fields of public health. Features: Illustrative, relevant examples and exercises incorporated throughout the book. Answers to odd-numbered exercises provided in the back of the book. (Instructors may requests answers to even-numbered exercises from the publisher. Chapters are intentionally brief and limited in scope to allow for flexibility in the order of coverage. Equal attention is given to manual calculations as well as the use of statistical software such as StaTable, SPSS, and WinPepi. Comprehensive Companion Website with Student and Instructor's Resources. |
cornell biostatistics and data science: Leadership in Statistics and Data Science Amanda L. Golbeck, 2021-03-22 This edited collection brings together voices of the strongest thought leaders on diversity, equity and inclusion in the field of statistics and data science, with the goal of encouraging and steering the profession into the regular practice of inclusive and humanistic leadership. It provides futuristic ideas for promoting opportunities for equitable leadership, as well as tested approaches that have already been found to make a difference. It speaks to the challenges and opportunities of leading successful research collaborations and making strong connections within research teams. Curated with a vision that leadership takes a myriad of forms, and that diversity has many dimensions, this volume examines the nuances of leadership within a workplace environment and promotes storytelling and other competencies as critical elements of effective leadership. It makes the case for inclusive and humanistic leadership in statistics and data science, where there often remains a dearth of women and members of certain racial communities among the employees. Titled and non-titled leaders will benefit from the planning, evaluation, and structural tools offered within to contribute inclusive excellence in workplace climate, environment, and culture. |
cornell biostatistics and data science: Plum and Posner's Diagnosis and Treatment of Stupor and Coma Jerome B. Posner, Clifford B. Saper, Nicholas D. Schiff, Jan Claassen, 2019 Plum and Posner's Diagnosis and Treatment of Stupor and Coma, 5th edition provides a comprehensive overview of the theory behind regulation of consciousness in humans, the mechanisms of loss of consciousness clinically, and the examination and diagnosis of the cause of loss of consciousness in patients. New sections provide the latest information on the treatment of comatose patients, brain death, recovery from structural coma, and the ethics of dealing with comatose patients. |
cornell biostatistics and data science: Financial Signal Processing and Machine Learning Ali N. Akansu, Sanjeev R. Kulkarni, Dmitry M. Malioutov, 2016-04-21 The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community. |
cornell biostatistics and data science: Next Generation Sequencing Lee-Jun C. Wong, 2013-05-31 In recent years, owing to the fast development of a variety of sequencing technologies in the post human genome project era, sequencing analysis of a group of target genes, entire protein coding regions of the human genome, and the whole human genome has become a reality. Next Generation Sequencing (NGS) or Massively Parallel Sequencing (MPS) technologies offers a way to screen for mutations in many different genes in a cost and time efficient manner by deep coverage of the target sequences. This novel technology has now been applied to clinical diagnosis of Mendelian disorders of well characterized or undefined diseases, discovery of new disease genes, noninvasive prenatal diagnosis using maternal blood, and population based carrier testing of severe autosomal recessive disorders. This book covers topics of these applications, including potential limitations and expanded application in the future. |
cornell biostatistics and data science: Targeted Learning in Data Science Mark J. van der Laan, Sherri Rose, 2018-03-28 This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011. Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics. Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics. |
cornell biostatistics and data science: Analysis of Observational Health Care Data Using SAS Douglas E. Faries, Andrew C. Leon, Josep Maria Haro, Robert L. Obenchain, 2010 This book guides researchers in performing and presenting high-quality analyses of all kinds of non-randomized studies, including analyses of observational studies, claims database analyses, assessment of registry data, survey data, pharmaco-economic data, and many more applications. The text is sufficiently detailed to provide not only general guidance, but to help the researcher through all of the standard issues that arise in such analyses. Just enough theory is included to allow the reader to understand the pros and cons of alternative approaches and when to use each method. The numerous contributors to this book illustrate, via real-world numerical examples and SAS code, appropriate implementations of alternative methods. The end result is that researchers will learn how to present high-quality and transparent analyses that will lead to fair and objective decisions from observational data. This book is part of the SAS Press program. |
cornell biostatistics and data science: Object Oriented Data Analysis J. S. Marron, Ian L. Dryden, 2021-11-18 Object Oriented Data Analysis is a framework that facilitates inter-disciplinary research through new terminology for discussing the often many possible approaches to the analysis of complex data. Such data are naturally arising in a wide variety of areas. This book aims to provide ways of thinking that enable the making of sensible choices. The main points are illustrated with many real data examples, based on the authors' personal experiences, which have motivated the invention of a wide array of analytic methods. While the mathematics go far beyond the usual in statistics (including differential geometry and even topology), the book is aimed at accessibility by graduate students. There is deliberate focus on ideas over mathematical formulas. J. S. Marron is the Amos Hawley Distinguished Professor of Statistics, Professor of Biostatistics, Adjunct Professor of Computer Science, Faculty Member of the Bioinformatics and Computational Biology Curriculum and Research Member of the Lineberger Cancer Center and the Computational Medicine Program, at the University of North Carolina, Chapel Hill. Ian L. Dryden is a Professor in the Department of Mathematics and Statistics at Florida International University in Miami, has served as Head of School of Mathematical Sciences at the University of Nottingham, and is joint author of the acclaimed book Statistical Shape Analysis. |
cornell biostatistics and data science: Teaching in the Hospital Jeff Wiese, 2010 Written by experts in the field, this text offers a unique perspective on the goals of inpatient teaching and practical advice for hospitalists and attendings who teach on the wards. |
cornell biostatistics and data science: Mechanisms of Differentiation Paul B. Fisher, 1990-09-20 Significant recent advances in cell culture technology now permit a detailed biochemical and molecular analysis of differentiation in both normal and tumor cells. These studies are important in attempting to understand the complex factors involved in normal growth and development, as well as the abnormalities associated with carcinogenesis. Mechanisms of Differentiation, Volumes I and II, is comprised of review chapters addressing various topics of current interest in this important area of research. Topics discussed include genes controlling differentiation, changes in gene expression during differentiation, induction of differentiation, induction of differentiation as a mode of action of chemotherapeutic agents, and the effect of cell shape, growth factors and differentiation modulating agents on the differentiated cell phenotype. Mechanisms of Differentiation is valuable to researchers involved in differentiation and development, carcinogenesis, cell biology, chemotherapy, and immunology. |
cornell biostatistics and data science: Statistics and Data Analysis for Financial Engineering David Ruppert, 2010-11-08 Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnosing modeling errors. Despite some overlap with the author's undergraduate textbook Statistics and Finance: An Introduction, this book differs from that earlier volume in several important aspects: it is graduate-level; computations and graphics are done in R; and many advanced topics are covered, for example, multivariate distributions, copulas, Bayesian computations, VaR and expected shortfall, and cointegration. The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus. Some exposure to finance is helpful. |
cornell biostatistics and data science: Data Monitoring Committees in Clinical Trials Susan S. Ellenberg, Thomas R. Fleming, David L. DeMets, 2003-01-17 There has been substantial growth in the use of data monitoring committees in recent years, by both government agencies and the pharmaceutical industry. This growth has been brought about by increasing recognition of the value of such committees in safeguarding trial participants as well as protecting trial integrity and the validity of conclusions. This very timely book describes the operation of data monitoring committees, and provides an authoritative guide to their establishment, purpose and responsibilities. * Provides a practical overview of data monitoring in clinical trials. * Describes the purpose, responsibilities and operation of data monitoring committees. * Provides directly applicable advice for those managing and conducting clinical trials, and those serving on data monitoring committees. * Gives insight into clinical data monitoring to those sitting on regulatory and ethical committees. * Discusses issues pertinent to those working in clinical trials in both the US and Europe. The practical guidance provided by this book will be of use to professionals working in and/or managing clinical trials, in academic, government and industry settings, particularly medical statisticians, clinicians, trial co-ordinators, and those working in regulatory affairs and bioethics. |
cornell biostatistics and data science: Encyclopedia of Medical Decision Making Michael W. Kattan, Mark E. Cowen, 2009-08-18 The Encyclopedia of Medical Decision Making presents state-of-the-art research and ready-to-use facts sorting out findings on medical decision making and their applications. |
cornell biostatistics and data science: Mobile Health James M. Rehg, Susan A. Murphy, Santosh Kumar, 2017-07-12 This volume provides a comprehensive introduction to mHealth technology and is accessible to technology-oriented researchers and practitioners with backgrounds in computer science, engineering, statistics, and applied mathematics. The contributing authors include leading researchers and practitioners in the mHealth field. The book offers an in-depth exploration of the three key elements of mHealth technology: the development of on-body sensors that can identify key health-related behaviors (sensors to markers), the use of analytic methods to predict current and future states of health and disease (markers to predictors), and the development of mobile interventions which can improve health outcomes (predictors to interventions). Chapters are organized into sections, with the first section devoted to mHealth applications, followed by three sections devoted to the above three key technology areas. Each chapter can be read independently, but the organization of the entire book provides a logical flow from the design of on-body sensing technology, through the analysis of time-varying sensor data, to interactions with a user which create opportunities to improve health outcomes. This volume is a valuable resource to spur the development of this growing field, and ideally suited for use as a textbook in an mHealth course. |
Master of Science Programs - Cornell University
Career Services help MS students build professional development skills to continue career growth and professional success during and … See more
Data Drives Healthcare - Amazon Web Services, Inc.
data insights that impact patient care. Master the intricate relationship between healthcare policy and economics and learn to influence policy from an analytical or leadership position. Where …
Biostatistics & Data Science - Weill Cornell Graduate School of …
Able to understand data generating processes, pros and cons of different study designs, bias and confounding and proficiency in critically reviewing and evaluating a study.
Biostatics & Data Science Assessment Response Form
Understand data generating processes, pros and cons of different study designs, bias and confounding and proficiency in critically reviewing and evaluating a study. 4.
Cornell Biostatistics And Data Science - mdghs.com
Cornell University offers a wealth of opportunities in Biostatistics and Data Science, attracting students and researchers from diverse backgrounds. This guide provides a comprehensive …
Weill Cornell Medical College Weill Cornell Graduate School …
Anderson, Ashley Maree, M.S., Biostatistics and Data Science. Advisor: Karla Ballman. Thesis: National Cancer Database to Build a Subset of Patients that Closely Match the Inclusion and …
DIVISION OF NUTRITIONAL SCIENCES - Cornell College of …
Research facilities such as the Human Metabolic Research Unit, Cornell Statistical Consulting Unit, and the Cornell Biotechnology Research Center provide access to cutting edge methods, …
Post-doctoral Associate in Mental Health Data Science.
Data Science is transforming mental health research by making mental health preventable and improving detection, screening and diagnosis of mental health conditions. The position at Weill …
Master of Science Programs in Population Health Sciences
Sep 4, 2024 · Weill Cornell Medicine and advance the Department’s mission to improve the health of populations through data-driven research, innovative technology, and novel educational …
Foundations of Data Science - Department of Computer Science
Contents 1 Introduction 9 2 High-Dimensional Space 12 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12 2.2 The Law of Large ...
Weill Cornell Medical College Weill Cornell Graduate School …
Cheng, Jiayue, M.S., Biostatistics and Data Science. Advisor: Wodan Ling. Thesis: Comparing Transcriptomic Signature of Single- and Clustered-Circulating Tumor Cells
Cornell Biostatistics And Data Science - origin-biomed.waters
cornell biostatistics and data science: Introduction to Data Science Laura Igual, Santi Seguí, 2017-02-22 This accessible and classroom-tested textbook/reference presents an introduction …
Cornell Biostatistics And Data Science - v4.jpopasia.com
Graduates from Cornell's Biostatistics and Data Science programs pursue diverse and rewarding careers, including: Biostatistician in Pharmaceutical Companies: Conducting clinical trials and …
The Clinical and Translational Science Center (CTSC) - Cornell …
i2b2 (Informatics for Integrating Biology and the Bedside) enables researchers to query de-identified data from the electronic health record and research systems at WCM to discover …
CERTIFICATE IN THE ANALYSIS OF MEDICAL DATA: APPLIED …
Intermediate Applied Biostatistics for Health Care Professionals The Weill Cornell Medicine-Qatar designates this live activity for a maximum of 6.50 AMA PRA Category 1 Credits ™.
Florentina Bunea - bpb-us-w2.wpmucdn.com
Foundations of data science; statistical machine learning theory. High dimensional statistical inference in parametric and non-parametric models: regression, covariance estimation, …
Curriculum Vitae James G. Booth - Cornell University …
Apr 6, 2016 · Chair: Department of Biological Statistics and Computational Biology, College of Agriculture and Life Sciences, Cornell University, July 2007{ June 2016. Director of …
Anisha Tehim - khuranalab.med.cornell.edu
Minor: Computer Science Relevant Courses: BIOCB4830 (Quantitative Genomics & Genetics), STSCI4030 (Linear Models with Matrices), STSCI3740 (Data Mining & Machine Learning), …
Updated November 2022 Will Simmons - GitHub Pages
2021 Simmons W*. \data.table and dtplyr: R packages for high-e ciency data processing." Invited lecture, Data Science I, Weill Cornell M.S. Biostatistics and Data Science.
Chief, Division of Biostatistics Department of Population …
generation of biostatisticians and data scientists. The Division Chief will play a leadership role in the design and execution of foundational, translational, clinical, and health services research …
Master of Science Programs - Cornell University
The MS in Biostatistics and Data Science provides comprehensive and rigorous training in statistical concepts and programming essential to tackling challenges in today’s data-driven world.
Data Drives Healthcare - Amazon Web Services, Inc.
data insights that impact patient care. Master the intricate relationship between healthcare policy and economics and learn to influence policy from an analytical or leadership position. Where …
Biostatistics & Data Science - Weill Cornell Graduate School …
Able to understand data generating processes, pros and cons of different study designs, bias and confounding and proficiency in critically reviewing and evaluating a study.
Biostatics & Data Science Assessment Response Form
Understand data generating processes, pros and cons of different study designs, bias and confounding and proficiency in critically reviewing and evaluating a study. 4.
Cornell Biostatistics And Data Science - mdghs.com
Cornell University offers a wealth of opportunities in Biostatistics and Data Science, attracting students and researchers from diverse backgrounds. This guide provides a comprehensive …
Weill Cornell Medical College Weill Cornell Graduate School …
Anderson, Ashley Maree, M.S., Biostatistics and Data Science. Advisor: Karla Ballman. Thesis: National Cancer Database to Build a Subset of Patients that Closely Match the Inclusion and …
DIVISION OF NUTRITIONAL SCIENCES - Cornell College of …
Research facilities such as the Human Metabolic Research Unit, Cornell Statistical Consulting Unit, and the Cornell Biotechnology Research Center provide access to cutting edge methods, …
Post-doctoral Associate in Mental Health Data Science.
Data Science is transforming mental health research by making mental health preventable and improving detection, screening and diagnosis of mental health conditions. The position at Weill …
Master of Science Programs in Population Health Sciences
Sep 4, 2024 · Weill Cornell Medicine and advance the Department’s mission to improve the health of populations through data-driven research, innovative technology, and novel educational …
Foundations of Data Science - Department of Computer …
Contents 1 Introduction 9 2 High-Dimensional Space 12 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12 2.2 The Law of Large ...
Weill Cornell Medical College Weill Cornell Graduate School …
Cheng, Jiayue, M.S., Biostatistics and Data Science. Advisor: Wodan Ling. Thesis: Comparing Transcriptomic Signature of Single- and Clustered-Circulating Tumor Cells
Cornell Biostatistics And Data Science - origin-biomed.waters
cornell biostatistics and data science: Introduction to Data Science Laura Igual, Santi Seguí, 2017-02-22 This accessible and classroom-tested textbook/reference presents an introduction to the …
Cornell Biostatistics And Data Science - v4.jpopasia.com
Graduates from Cornell's Biostatistics and Data Science programs pursue diverse and rewarding careers, including: Biostatistician in Pharmaceutical Companies: Conducting clinical trials and …
The Clinical and Translational Science Center (CTSC)
i2b2 (Informatics for Integrating Biology and the Bedside) enables researchers to query de-identified data from the electronic health record and research systems at WCM to discover …
CERTIFICATE IN THE ANALYSIS OF MEDICAL DATA: …
Intermediate Applied Biostatistics for Health Care Professionals The Weill Cornell Medicine-Qatar designates this live activity for a maximum of 6.50 AMA PRA Category 1 Credits ™.
Florentina Bunea - bpb-us-w2.wpmucdn.com
Foundations of data science; statistical machine learning theory. High dimensional statistical inference in parametric and non-parametric models: regression, covariance estimation, graphical …
Curriculum Vitae James G. Booth - Cornell University …
Apr 6, 2016 · Chair: Department of Biological Statistics and Computational Biology, College of Agriculture and Life Sciences, Cornell University, July 2007{ June 2016. Director of …
Anisha Tehim - khuranalab.med.cornell.edu
Minor: Computer Science Relevant Courses: BIOCB4830 (Quantitative Genomics & Genetics), STSCI4030 (Linear Models with Matrices), STSCI3740 (Data Mining & Machine Learning), …
Updated November 2022 Will Simmons - GitHub Pages
2021 Simmons W*. \data.table and dtplyr: R packages for high-e ciency data processing." Invited lecture, Data Science I, Weill Cornell M.S. Biostatistics and Data Science.
Chief, Division of Biostatistics Department of Population …
generation of biostatisticians and data scientists. The Division Chief will play a leadership role in the design and execution of foundational, translational, clinical, and health services research …