To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. A masterpiece from the master!. If you are only ever going to buy one statistics book, or if you are thinking of updating your library and retiring a dozen or so dusty stats texts, this book would be an excellent choice. He has held visiting faculty appointments at Harvard University, Massachusetts, the University of California, Berkeley, and Imperial College of Science, Technology and Medicine, London. It gives a clear, accessible, and entertaining account of the interplay between theory and methodological development that has driven statistics in the computer age. Computer Age Statistical Inference: Algorithms, Evidence and Data Science by Bradley Efron and Trevor Hastie is a brilliant read. Koop Computer Age Statistical Inference van Efron, Bradley, met ISBN 9781107149892. Bradley Efron (/ ˈ ɛ f r ən /; born May 24, 1938) is an American statistician. Just amazing. D. J. Gougeon, Choice, ©1997-2020 Barnes & Noble Booksellers, Inc. 122 Fifth Avenue, New York, NY 10011. 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. Prime members enjoy FREE Delivery and exclusive access to music, movies, TV shows, original audio series, and Kindle books. — Carl Morris, Harvard University Computer Age Statistical InferenceThe twenty-first century has seen a breathtaking expansion of statistical methodology,both in scope and in influence. Focusing primarily on the last six decades, the text thoroughly documents the progression within the discipline of statistics … This text is highly recommended for graduate libraries.' The methods covered are indispensable to practicing statistical analysts in today’s big data and big computing landscape.’ In order to navigate out of this carousel please use your heading shortcut key to navigate to the next or previous heading. Totally satisfied with the order, it arrived fast, and is of high quality. If you are only ever going to buy one statistics book, or if you are thinking of updating your library and retiring a dozen or so dusty stats texts, this book would be an excellent choice. Take an exhilarating journey through the modern revolution in statistics with two of the ringleaders. Excellent review of Statistical inference. Highlighting their origins, the book helps us understand each method's roles in inference and/or prediction. Reviewed in the United States on November 28, 2017. 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. Gratis verzending, Slim studeren. Reviewed in the United Kingdom on September 2, 2017. Computer Age Statistical Inference: Algorithms, Evidence and Data Science. 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. Alastair Young, Imperial College London"This is a guided tour of modern statistics that emphasizes the conceptual and computational advances of the last century. The book seamlessly integrates statistical thinking with computational thinking, while covering a broad range of powerful algorithms for learning from data. I own and have read the authors' other books and as always, this new book is fantastic. Very insightful and informative statistical inference book. It made the topics that I struggled for many years extremely easy. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science: Efron, Bradley, Hastie, Trevor: Amazon.nl Selecteer uw cookievoorkeuren We gebruiken cookies en vergelijkbare tools om uw winkelervaring te verbeteren, onze services aan te bieden, te begrijpen hoe klanten onze services gebruiken zodat we verbeteringen kunnen aanbrengen, en om advertenties weer te geven. Please try again. Bradley Efron is Max H. Stein Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University, California. And where are we going? Reviewed in the United States on April 9, 2018. Bring your club to Amazon Book Clubs, start a new book club and invite your friends to join, or find a club that’s right for you for free. The work examines major developments in computation from the late-20th and early-21st centuries, ranging from electronic computations to 'big data' analysis. Computer Age Statistical Inference: Algorithms, Evidence and Data Science by Bradley Efron and Trevor Hastie is a brilliant read. "How and why is computational statistics taking over the world? Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Computer Age Statistical Inference by Bradley Efron & Trevor Hastie is Mathematics The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. Algorithmics are put on equal footing with intuition, properties, and the abstract arguments behind them. Bradley Efron is Max H. Stein Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University, California. '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. Reviewed in the United Kingdom on October 12, 2016. if you knew a bit of statistics lingo, and could get your head round the maths, this book is a thriller. Rob Kass, Carnegie Mellon University, Pennsylvania"This is a terrific book. This book is a must have for mathematically sophisticated readers wanting to expand their knowledge of traditional statistical inference techniques as well as inference done by machine learning. I read the book like a novel. "Computer Age Statistical Inference offers a refreshing view of modern statistics. Reviewed in the United States on August 7, 2017. 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. The authors succeed brilliantly in locating contemporary algorithmic methodologies for analysis of 'big data' within the framework of established statistical theory." The book ends with speculation on the future direction of statistics and data science. The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 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. How did we get here? It will take time to digest it all ..... thank you Prof Efron! He received the National Medal of Science in 2005 and the Guy Medal in Gold of the Royal Statistical Society in 2014. Something went wrong. In 475 carefully crafted pages, Efron and … It makes a great supplement to the traditional curricula for beginning graduate students." The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs, Series Number 5). Enabling JavaScript in your browser will allow you to experience all the features of our site. 2016-10-28. by Joseph Rickert. A fantastic book that summarizes the statistical inference techniques. 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." This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Algorithmics are put on equal footing with intuition, properties, and the abstract arguments behind them. Regression and Other Stories (Analytical Methods for Social Research), Causal Inference in Statistics - A Primer, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics), Statistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman & Hall/CRC Texts in Statistical Science), An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics), High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 48), Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series), "How and why is computational statistics taking over the world? Electronic computations to 'big data ' analysis totally satisfied with the order arrived in time and has a of!, Columbia University, California framework of established statistical theory. 5 ) students. computation in United! 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