regression modeling strategies

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Regression Modeling Strategies

Author : Frank E. Harrell
ISBN : 9781475734621
Genre : Mathematics
File Size : 62. 5 MB
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Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

Regression Modeling Strategies

Author : Frank E. Harrell , Jr.
ISBN : 9783319194257
Genre : Mathematics
File Size : 61. 95 MB
Format : PDF, ePub
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This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modelling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasises problem solving strategies that address the many issues arising when developing multi-variable models using real data and not standard textbook examples. Regression Modelling Strategies presents full-scale case studies of non-trivial data-sets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalised least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression. As in the first edition, this text is intended for Masters' or PhD. level graduate students who have had a general introductory probability and statistics course and who are well versed in ordinary multiple regression and intermediate algebra. The book will also serve as a reference for data analysts and statistical methodologists, as it contains an up-to-date survey and bibliography of modern statistical modelling techniques.

Logistic Regression Inside And Out

Author : Jeffrey Strickland
ISBN : 9781365819155
Genre :
File Size : 46. 79 MB
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Applied Logistic Regression

Author : David W. Hosmer, Jr.
ISBN : 9781118548356
Genre : Mathematics
File Size : 75. 35 MB
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A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: A chapter on the analysis of correlated outcome data A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.

Selected Papers Of Frederick Mosteller

Author : Stephen E. Fienberg
ISBN : 9780387449562
Genre : Mathematics
File Size : 54. 56 MB
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One of the best known statisticians of the 20th century, Frederick Mosteller has inspired numerous statisticians and other scientists by his creative approach to statistics and its applications. This volume collects 40 of his most original and influential papers, capturing the variety and depth of his writings. It is hoped that sharing these writings with a new generation of researchers will inspire them to build upon his insights and efforts.

Modeling Techniques In Predictive Analytics With Python And R

Author : Thomas W. Miller
ISBN : 9780133892147
Genre : Computers
File Size : 78. 17 MB
Format : PDF, ePub, Docs
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Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Modeling Techniques In Predictive Analytics

Author : Thomas W. Miller
ISBN : 9780133886191
Genre : Computers
File Size : 36. 12 MB
Format : PDF
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To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Penalty Shrinkage And Pretest Strategies

Author : S. Ejaz Ahmed
ISBN : 9783319031491
Genre : Mathematics
File Size : 64. 49 MB
Format : PDF
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The objective of this book is to compare the statistical properties of penalty and non-penalty estimation strategies for some popular models. Specifically, it considers the full model, submodel, penalty, pretest and shrinkage estimation techniques for three regression models before presenting the asymptotic properties of the non-penalty estimators and their asymptotic distributional efficiency comparisons. Further, the risk properties of the non-penalty estimators and penalty estimators are explored through a Monte Carlo simulation study. Showcasing examples based on real datasets, the book will be useful for students and applied researchers in a host of applied fields. The book’s level of presentation and style make it accessible to a broad audience. It offers clear, succinct expositions of each estimation strategy. More importantly, it clearly describes how to use each estimation strategy for the problem at hand. The book is largely self-contained, as are the individual chapters, so that anyone interested in a particular topic or area of application may read only that specific chapter. The book is specially designed for graduate students who want to understand the foundations and concepts underlying penalty and non-penalty estimation and its applications. It is well-suited as a textbook for senior undergraduate and graduate courses surveying penalty and non-penalty estimation strategies, and can also be used as a reference book for a host of related subjects, including courses on meta-analysis. Professional statisticians will find this book to be a valuable reference work, since nearly all chapters are self-contained.

Epidemiologic Methods

Author : Noel S. Weiss
ISBN : 9780199387786
Genre : Medical
File Size : 46. 8 MB
Format : PDF, Kindle
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This second edition of Epidemiologic Methods offers a rigorous introduction to the concepts and tools of epidemiologic research. Aimed chiefly at future epidemiologists, the book offers clear descriptions, practical examples, and question/answer sections for each of the science's key concepts. Authored by two award-winning epidemiology instructors, this book is ideally suited for use as a text in a graduate-level course sequence in epidemiologic methods. The book's chapters are organized around three main themes: general concepts and tools of epidemiology; major study designs; and special topics, including screening, outbreak investigations, and use of epidemiology to evaluate policies and programs. With additional exercises at the end of each chapter and expanded attention to topics such as confounding, this new edition of Epidemiologic Methods is an indispensable resource for the next generation of epidemiologic study.

Handbook Of Health Survey Methods

Author : Timothy P. Johnson
ISBN : 9781118594742
Genre : Medical
File Size : 54. 34 MB
Format : PDF, ePub, Mobi
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A comprehensive guidebook to the current methodologiesand practices used in health surveys A unique and self-contained resource, Handbook of HealthSurvey Methods presents techniques necessary for confrontingchallenges that are specific to health survey research. Thehandbook guides readers through the development of sample designs,data collection procedures, and analytic methods for studies aimedat gathering health information on general and targetedpopulations. The book is organized into five well-defined sections: Design andSampling Issues, Measurement Issues, Field Issues, Health Surveysof Special Populations, and Data Management and Analysis.Maintaining an easy-to-follow format, each chapter begins with anintroduction, followed by an overview of the main concepts,theories, and applications associated with each topic. Finally,each chapter provides connections to relevant online resources foradditional study and reference. The Handbook of Health SurveyMethods features: 29 methodological chapters written by highly qualified expertsin academia, research, and industry A treatment of the best statistical practices and specificmethodologies for collecting data from special populations such assexual minorities, persons with disabilities, patients, andpractitioners Discussions on issues specific to health research includingdeveloping physical health and mental health measures, collectinginformation on sensitive topics, sampling for clinical trials,collecting biospecimens, working with proxy respondents, andlinking health data to administrative and other external datasources Numerous real-world examples from the latest research in thefields of public health, biomedicine, and health psychology Handbook of Health Survey Methods is an ideal reference foracademics, researchers, and practitioners who apply survey methodsand analyze data in the fields of biomedicine, public health,epidemiology, and biostatistics. The handbook is also a usefulsupplement for upper-undergraduate and graduate-level courses onsurvey methodology.

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