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Many of the concepts and terminology surrounding modern causal inference can be quite intimidating to the novice. Judea Pearl presents a book ideal for beginners in statistics, providing a comprehensive introduction to the field of causality. Examples from classical statistics are presented throughout to demonstrate the need for causality in resolving decision-making dilemmas posed by data. Causal methods are also compared to traditional statistical methods, whilst questions are provided at the end of each section to aid student learning.
This state-of-the-art survey is dedicated to the memory of Emmanuil Markovich Braverman (1931-1977), a pioneer in developing machine learning theory. The 12 revised full papers and 4 short papers included in this volume were presented at the conference "Braverman Readings in Machine Learning: Key Ideas from Inception to Current State" held in Boston, MA, USA, in April 2017, commemorating the 40th anniversary of Emmanuil Braverman's decease. The papers present an overview of some of Braverman's ideas and approaches. The collection is divided in three parts. The first part bridges the past and the present and covers the concept of kernel function and its application to signal and image analysis as well as clustering. The second part presents a set of extensions of Braverman's work to issues of current interest both in theory and applications of machine learning. The third part includes short essays by a friend, a student, and a colleague.
This book brings together a collection of articles onstatistical methods relating to missing data analysis, includingmultiple imputation, propensity scores, instrumental variables, andBayesian inference. Covering new research topicsand real-world examples which do not feature in manystandard texts. The book is dedicated to Professor Don Rubin(Harvard). Don Rubin has made fundamental contributions tothe study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both researchand applications. Adopts a pragmatic approach to describing a wide range ofintermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensityscores, instrumental variables and Bayesian inference. Includes a number of applications from the social and healthsciences. Edited and authored by highly respected researchers in thearea.
The book provides an accessible but comprehensive overview of methods for mediation and interaction. There has been considerable and rapid methodological development on mediation and moderation/interaction analysis within the causal-inference literature over the last ten years. Much of this material appears in a variety of specialized journals, and some of the papers are quite technical. There has also been considerable interest in these developments from empirical researchers in the social and biomedical sciences. However, much of the material is not currently in a format that is accessible to them. The book closes these gaps by providing an accessible, comprehensive, book-length coverage of mediation. The book begins with a comprehensive introduction to mediation analysis, including chapters on concepts for mediation, regression-based methods, sensitivity analysis, time-to-event outcomes, methods for multiple mediators, methods for time-varying mediation and longitudinal data, and relations between mediation and other concepts involving intermediates such as surrogates, principal stratification, instrumental variables, and Mendelian randomization. The second part of the book concerns interaction or "moderation," including concepts for interaction, statistical interaction, confounding and interaction, mechanistic interaction, bias analysis for interaction, interaction in genetic studies, and power and sample-size calculation for interaction. The final part of the book provides comprehensive discussion about the relationships between mediation and interaction and unites these concepts within a single framework. This final part also provides an introduction to spillover effects or social interaction, concluding with a discussion of social-network analyses. The book is written to be accessible to anyone with a basic knowledge of statistics. Comprehensive appendices provide more technical details for the interested reader. Applied empirical examples from a variety of fields are given throughout. Software implementation in SAS, Stata, SPSS, and R is provided. The book should be accessible to students and researchers who have completed a first-year graduate sequence in quantitative methods in one of the social- or biomedical-sciences disciplines. The book will only presuppose familiarity with linear and logistic regression, and could potentially be used as an advanced undergraduate book as well.
A indispensable guide to understanding and designing modern experiments The tools and techniques of Design of Experiments (DOE) allow researchers to successfully collect, analyze, and interpret data across a wide array of disciplines. Statistical Analysis of Designed Experiments provides a modern and balanced treatment of DOE methodology with thorough coverage of the underlying theory and standard designs of experiments, guiding the reader through applications to research in various fields such as engineering, medicine, business, and the social sciences. The book supplies a foundation for the subject, beginning with basic concepts of DOE and a review of elementary normal theory statistical methods. Subsequent chapters present a uniform, model-based approach to DOE. Each design is presented in a comprehensive format and is accompanied by a motivating example, discussion of the applicability of the design, and a model for its analysis using statistical methods such as graphical plots, analysis of variance (ANOVA), confidence intervals, and hypothesis tests. Numerous theoretical and applied exercises are provided in each chapter, and answers to selected exercises are included at the end of the book. An appendix features three case studies that illustrate the challenges often encountered in real-world experiments, such as randomization, unbalanced data, and outliers. Minitab® software is used to perform analyses throughout the book, and an accompanying FTP site houses additional exercises and data sets. With its breadth of real-world examples and accessible treatment of both theory and applications, Statistical Analysis of Designed Experiments is a valuable book for experimental design courses at the upper-undergraduate and graduate levels. It is also an indispensable reference for practicing statisticians, engineers, and scientists who would like to further their knowledge of DOE.
Theories and practices to assess critical information in acomplex adaptive system Organized for readers to follow along easily, The Fitness ofInformation: Quantitative Assessments of Critical Evidenceprovides a structured outline of the key challenges in assessingcrucial information in a complex adaptive system. Illustrating avariety of computational and explanatory challenges, the bookdemonstrates principles and practical implications of exploring andassessing the fitness of information in an extensible framework ofadaptive landscapes. The book’s first three chapters introduce fundamentalprinciples and practical examples in connection to the nature ofaesthetics, mental models, and the subjectivity of evidence. Inparticular, the underlying question is how these issues can beaddressed quantitatively, not only computationally but alsoexplanatorily. The next chapter illustrates how one can reduce thelevel of complexity in understanding the structure and dynamics ofscientific knowledge through the design and use of the CiteSpacesystem for visualizing and analyzing emerging trends in scientificliterature. The following two chapters explain the concepts ofstructural variation and the fitness of information in a frameworkthat builds on the idea of fitness landscape originally introducedto study population evolution. The final chapter presents adual-map overlay technique and demonstrates how it supports avariety of analytic tasks for a new type of portfolioanalysis. The Fitness of Information: Quantitative Assessments of CriticalEvidence also features: In-depth case studies and examples that characterizefar-reaching concepts, illustrate underlying principles, anddemonstrate profound challenges and complexities at various levelsof analytic reasoning Wide-ranging topics that underline the common theme, from thesubjectivity of evidence in criminal trials to detecting earlysigns of critical transitions and mechanisms behind radicalpatents An extensible and unifying framework for visual analytics bytransforming analytic reasoning tasks to the assessment of criticalevidence The Fitness of Information: Quantitative Assessments of CriticalEvidence is a suitable reference for researchers, analysts, andpractitioners who are interested in analyzing evidence and makingdecisions with incomplete, uncertain, and even conflictinginformation. The book is also an excellent textbook forupper-undergraduate and graduate-level courses on visual analytics,information visualization, and business analytics and decisionsupport systems.
Praise for the Second Edition “This book should be an essential part of the personallibrary of every practicingstatistician.”—Technometrics Thoroughly revised and updated, the new edition of NonparametricStatistical Methods includes additional modern topics andprocedures, more practical data sets, and new problems fromreal-life situations. The book continues to emphasize theimportance of nonparametric methods as a significant branch ofmodern statistics and equips readers with the conceptual andtechnical skills necessary to select and apply the appropriateprocedures for any given situation. Written by leading statisticians, Nonparametric StatisticalMethods, Third Edition provides readers with crucialnonparametric techniques in a variety of settings, emphasizing theassumptions underlying the methods. The book provides an extensivearray of examples that clearly illustrate how to use nonparametricapproaches for handling one- or two-sample location and dispersionproblems, dichotomous data, and one-way and two-way layoutproblems. In addition, the Third Edition features: The use of the freely available R software to aid incomputation and simulation, including many new R programs writtenexplicitly for this new edition New chapters that address density estimation, wavelets,smoothing, ranked set sampling, and Bayesian nonparametrics Problems that illustrate examples from agricultural science,astronomy, biology, criminology, education, engineering,environmental science, geology, home economics, medicine,oceanography, physics, psychology, sociology, and spacescience Nonparametric Statistical Methods, Third Edition is anexcellent reference for applied statisticians and practitioners whoseek a review of nonparametric methods and their relevantapplications. The book is also an ideal textbook forupper-undergraduate and first-year graduate courses in appliednonparametric statistics.

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