visión de conjunto:Críticas 'How and why is computational statistics taking over the world? In this serious work of synthesis that is also fun to read Efron and Hastie two pioneers in the integration of parametric and nonparametric statistical ideas give their take on the unreasonable effectiveness of statistics and machine learning in the context of a series of clear historically informed examples.' Andrew Gelman Columbia University New York'This unusual book describes the nature of statistics by displaying multiple examples of the way the field has evolved over the past sixty years as it has adapted to the rapid increase in available computing power. The authors' perspective is summarized nicely when they say 'very roughly speaking algorithms are what statisticians do while inference says why they do them'. The book explains this 'why'; that is it explains the purpose and progress of statistical research through a close look at many major methods methods the authors themselves have advanced and studied at great length. Both enjoyable and enlightening Computer Age Statistical Inference is written especially for those who want to hear the big ideas and see them instantiated through the essential mathematics that defines statistical analysis. It makes a great supplement to the traditional curricula for beginning graduate students.' Rob Kass Carnegie Mellon University Pennsylvania'This is a terrific book. It gives a clear accessible and entertaining account of the interplay between theory and methodological development that has driven statistics in the computer age. The authors succeed brilliantly in locating contemporary algorithmic methodologies for analysis of 'big data' within the framework of established statistical theory.' Alastair Young Imperial College London'This is a guided tour of modern statistics that emphasizes the conceptual and computational advances of the last century. Authored by two masters of the field it offers just the right mix of mathematical analysis and insightful commentary.' Hal Varian Google'Efron and Hastie guide us through the maze of breakthrough statistical methodologies following the computing evolution: why they were developed their properties and how they are used. Highlighting their origins the book helps us understand each method's roles in inference and/or prediction. The inference-prediction distinction maintained throughout the book is a welcome and important novelty in the landscape of statistics books.' Galit Shmueli National Tsing Hua University'A masterful guide to how the inferential bases of classical statistics can provide a principled disciplinary frame for the data science of the twenty-first century.' Stephen Stigler University of Chicago and author of Seven Pillars of Statistical Wisdom'Computer Age Statistical Inference offers a refreshing view of modern statistics. Algorithmics are put on equal footing with intuition properties and the abstract arguments behind them. The methods covered are indispensable to practicing statistical analysts in today's big data and big computing landscape.' Robert Gramacy University of Chicago Booth School of Business'Every aspiring data scientist should carefully study this book use it as a reference and carry it with them everywhere. The presentation through the two-and-a-half-century history of statistical inference provides insight into the development of the discipline putting data science in its historical place.' Mark Girolami Imperial College London'Efron and Hastie are two immensely talented and accomplished scholars who have managed to brilliantly weave the fiber of 250 years of statistical inference into the more recent historical mechanization of computing. This book provides the reader with a mid-level overview of the last 60-some years by detailing the nuances of a statistical community that historically has been self-segregated into camps of Bayes frequentist and Fisher yet in more recent years has been unified by advances in computing. What is left to be explored is the emergence of and role that big data theory will have in bridging the gap between data science and statistical methodology. Whatever the outcome the authors provide a vision of high-speed computing having tremendous potential to enable the contributions of statistical inference toward methodologies that address both global and societal issues.' Rebecca Doerge Carnegie Mellon University Pennsylvania'In this book two masters of modern statistics give an insightful tour of the intertwined worlds of statistics and computation. Through a series of important topics Efron and Hastie illuminate how modern methods for predicting and understanding data are rooted in both statistical and computational thinking. They show how the rise of computational power has transformed traditional methods and questions and how it has pointed us to new ways of thinking about statistics.' David Blei Columbia University New York Reseña del editor The twenty-first century has seen a breathtaking expansion of statistical methodology both in scope and in influence. 'Big data' 'data science' and 'machine learning' have become familiar terms in the news as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian frequentist Fisherian - individual chapters take up a series of influential topics: survival analysis logistic regression empirical Bayes the jackknife and bootstrap random forests neural networks Markov chain Monte Carlo inference after model selection and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science. Descripción del libro Computing power has revolutionized the theory and practice of statistical inference. This book delivers a concentrated course in modern statistical thinking by tracking the revolution from classical theories to the large-scale prediction algorithms of today. Anyone who applies statistical methods to data will benefit from this landmark text. Biografía del autor Bradley Efron is Max H. Stein Professor Professor of Statistics and Professor of Biomedical Data Science at Stanford University California. He has held visiting faculty appointments at Harvard University Massachusetts the University of California Berkeley and Imperial College of Science Technology and Medicine London. Efron has worked extensively on theories of statistical inference and is the inventor of the bootstrap sampling technique. He received the National Medal of Science in 2005 and the Guy Medal in Gold of the Royal Statistical Society in 2014.Trevor Hastie is John A. Overdeck Professor Professor of Statistics and Professor of Biomedical Data Science at Stanford University California. He is coauthor of Elements of Statistical Learning a key text in the field of modern data analysis. He is also known for his work on generalized additive models and principal curves and for his contributions to the R computing environment. Hastie was awarded the Emmanuel and Carol Parzen prize for Statistical Innovation in 2014.