This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis depends in an integrated fashion on sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. The unifying theme is that supervised learning properly can be seen as a form of regression analysis. Key concepts and procedures are illustrated with a large number of real applications and their associated code in R, with an eye toward practical implications. The growing integration of computer science and statistics is well represented including the occasional, but salient, tensions that result. Throughout, there are links to the big picture. The third edition considers significant advances in recent years, among which are: the development of overarching, conceptual frameworks for statistical learning; the impact of “big data” on statistical learning; the nature and consequences of post-model selection statistical inference; deep learning in various forms; the special challenges to statistical inference posed by statistical learning; the fundamental connections between data collection and data analysis; interdisciplinary ethical and political issues surrounding the application of algorithmic methods in a wide variety of fields, each linked to concerns about transparency, fairness, and accuracy. This edition features new sections on accuracy, transparency, and fairness, as well as a new chapter on deep learning. Precursors to deep learning get an expanded treatment. The connections between fitting and forecasting are considered in greater depth. Discussion of the estimation targets for algorithmic methods is revised and expanded throughout to reflect the latest research. Resampling procedures are emphasized. The material is written for upper undergraduate and graduate students in the social, psychological and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems.

An authoritative reference on depression and mood disorders, this volume brings together the field's preeminent researchers. All aspects of unipolar and bipolar depression are addressed, from genetics, neurobiology, and social-contextual risk factors to the most effective approaches to assessment and clinical management. Contributors review what is known about depression in specific populations, exploring developmental issues across the lifespan as well as gender and cultural variables. Effective psychosocial and biological treatments are described in detail. Each chapter offers a definitive statement of current theories, methods, and findings, and identifies key questions that remain to be answered. New to This Edition *Incorporates cutting-edge research (including findings from international, multisite, integrative, and longitudinal studies), treatment advances, and changes to diagnostic criteria in DSM-5. *Chapters on comorbidity with anxiety disorders and emotional functioning in depression. *Expanded coverage of bipolar disorder, now the focus of three chapters (clinical features, risk and etiological factors, and treatment). *Many new authors and extensively revised chapters.

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. This up-to-the-minute reference will help you master all three facets of sports analytics — and use it to win! Sports Analytics and Data Science is the most accessible and practical guide to sports analytics for everyone who cares about winning and everyone who is interested in data science. You’ll discover how successful sports analytics blends business and sports savvy, modern information technology, and sophisticated modeling techniques. You’ll master the discipline through realistic sports vignettes and intuitive data visualizations–not complex math. Every chapter focuses on one key sports analytics application. Miller guides you through assessing players and teams, predicting scores and making game-day decisions, crafting brands and marketing messages, increasing revenue and profitability, and much more. Step by step, you’ll learn how analysts transform raw data and analytical models into wins: both on the field and in any sports business.

Fully updated fundamental biomedical engineering principles and technologies This state-of-the-art resource offers unsurpassed coverage of fundamental concepts that enable advances in the field of biomedical engineering. Biomedical Engineering Fundamentals, Third Edition, contains all the information you need to improve efficacy and efficiency in problem solving, no matter how simple or complex the problem. Thoroughly revised by experts across the biomedical engineering discipline, this hands-on guide provides the foundational knowledge required for the development of innovative devices, techniques, and treatments. Coverage includes: Modeling of biomedical systems and heat transfer applications Physical and flow properties of blood Respiratory mechanics and gas exchange Respiratory muscles, human movement, and the musculoskeletal system Electromyography and muscle forces Biopolymers, biomedical composites, and bioceramics Cardiovascular, dental, and orthopedic biomaterials Tissue regeneration and regenerative medicine Bioelectricity, biomedical signal analysis, and biosensors Neural engineering and electrical stimulation of nervous systems Causes of medical device failure and FDA requirements Cardiovascular, respiratory, and artificial kidney devices Infrared and ultrasound imaging, MRIs, and nuclear medicine Imaging, laser Doppler, and fetal and optical monitoring Computer-integrated surgery and medical robotics Intelligent assistive technology and rehabilitators Artificial limbs, hip and knee replacement, and sensory augmentation Healthcare systems engineering and medical informatics Hospital information systems and computer-based patient records Sterile medical device package development

This book presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models, in a language that is familiar to practicing statisticians. Questions arise when statisticians are first confronted with such a model, and this book's aim is to provide thorough answers.

Over the past decade or so, neural computation has emerged as a research area with active involvement by researchers from a number of different disciplines, including computer science, engineering, mathematics, neurobiology, physics, and statistics. The workshop brought together researchers with a diverse background to review the current status of neural computation research. Three aspects of neural computation have been emphasized: neuroscience aspects, computational and Mathematical aspects, and statistical physics aspects. This book contains 28 contributions from frontier researchers in these fields. Thoroughly re-edited, and in some cases revised post-workshop, these papers collated into this review volume provide a top-class reference summary of the state-of-the-art work done in this field.

The First Edition of "Essentials of Business Statistics" delivers clear and understandable explanations of essential business statistics concepts through the use of case studies and examples. Along with the text, this edition offers a wide range of supplements that bring greater clarity to the text's concepts while also giving you the flexibility of additional coursework. -- From publisher's description.