Computer Age Statistical Inference

Algorithms, Evidence, and Data Science

Nonfiction, Science & Nature, Mathematics, Statistics, Computers, General Computing
Cover of the book Computer Age Statistical Inference by Bradley Efron, Trevor Hastie, Cambridge University Press
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Author: Bradley Efron, Trevor Hastie ISBN: 9781108105910
Publisher: Cambridge University Press Publication: July 20, 2016
Imprint: Cambridge University Press Language: English
Author: Bradley Efron, Trevor Hastie
ISBN: 9781108105910
Publisher: Cambridge University Press
Publication: July 20, 2016
Imprint: Cambridge University Press
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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