Why did I love this book?
The text covers classic statistical inference, early computer-age methods, and twenty-century topics. This puts a unique perspective on current analytic technologies labeled machine learning, artificial intelligence, and statical learning. The examples used provide a powerful description of the methods covered and the compare and contrast sections highlight the evolution of analytics. This book by Efron and Hastie is a natural follow-up source for readers interested in more details.
2 authors picked Computer Age Statistical Inference, Algorithms, Evidence, and Data Science as one of their favorite books, and they share why you should read it.
The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. '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? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic…