Computer age statistical inference algorithms, evidence, and data science / Bradley Efron, Trevor Hastie.

By:

Efron, Bradley [author.]

Contributor(s):

Hastie, Trevor [author.]

Material type: Text

TextSeries: Institute of Mathematical Statistics monographs ; 6.Publisher: Cambridge : Cambridge University Press, 2021Edition: Student editionDescription: 1 online resource (xix, 492 pages) : digital, PDF file(s)Content type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9781108823418

Subject(s):

Additional physical formats: Print version: : No titleDDC classification:

519.502 EFR 2021

Summary: 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 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. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.

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Item type	Current library	Call number	Status	Date due	Barcode
Book	Symbiosis International University, Dubai	519.502 EFR 2021 (Browse shelf(Opens below))	Available		SIU00065

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Title from publisher's bibliographic system (viewed on 26 Oct 2021).

Online version restricted to NUS staff and students only through NUSNET.

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 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. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.

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