Skip to main content
×
×
Home
Computer Age Statistical Inference
  • Get access
    Check if you have access via personal or institutional login
  • Cited by 64
  • Cited by
    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Neunhoeffer, Marcel and Sternberg, Sebastian 2019. How Cross-Validation Can Go Wrong and What to Do About It. Political Analysis, Vol. 27, Issue. 01, p. 101.

    Sharifi-Malvajerdi, Saeed Zhu, Feiyu Fogarty, Colin B. Fay, Michael P. Fairhurst, Rick M. Flegg, Jennifer A. Stepniewska, Kasia and Small, Dylan S. 2019. Malaria parasite clearance rate regression: an R software package for a Bayesian hierarchical regression model. Malaria Journal, Vol. 18, Issue. 1,

    MacCormick, Ian J. C. Williams, Bryan M. Zheng, Yalin Li, Kun Al-Bander, Baidaa Czanner, Silvester Cheeseman, Rob Willoughby, Colin E. Brown, Emery N. Spaeth, George L. Czanner, Gabriela and Bhattacharya, Sanjoy 2019. Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile. PLOS ONE, Vol. 14, Issue. 1, p. e0209409.

    Berk, Richard 2019. Machine Learning Risk Assessments in Criminal Justice Settings. p. 57.

    Zhao, Yue Hryniewicki, Maciej K. Cheng, Francesca Fu, Boyang and Zhu, Xiaoyu 2019. Intelligent Systems and Applications. Vol. 869, Issue. , p. 737.

    Duraisamy, Karthik Iaccarino, Gianluca and Xiao, Heng 2019. Turbulence Modeling in the Age of Data. Annual Review of Fluid Mechanics, Vol. 51, Issue. 1, p. 357.

    Sverdlov, Oleksandr van Dam, Joris Hannesdottir, Kristin and Thornton-Wells, Tricia 2018. Digital Therapeutics: An Integral Component of Digital Innovation in Drug Development. Clinical Pharmacology & Therapeutics, Vol. 104, Issue. 1, p. 72.

    Tibshirani, Ryan J. and Rosset, Saharon 2018. Excess Optimism: How Biased is the Apparent Error of an Estimator Tuned by SURE?. Journal of the American Statistical Association, p. 1.

    Li, Yi and Ding, A. Adam 2018. Double-structured sparse multitask regression with application of statistical downscaling. Environmetrics, p. e2534.

    Takato, Yasuno 2018. Trends and Applications in Knowledge Discovery and Data Mining. Vol. 11154, Issue. , p. 357.

    Mandel, Igor 2018. Braverman Readings in Machine Learning. Key Ideas from Inception to Current State. Vol. 11100, Issue. , p. 148.

    Davies, Alec Green, Mark A. Singleton, Alex D. and Rao, Praveen 2018. Using machine learning to investigate self-medication purchasing in England via high street retailer loyalty card data. PLOS ONE, Vol. 13, Issue. 11, p. e0207523.

    Callies, Jörn 2018. Restratification of Abyssal Mixing Layers by Submesoscale Baroclinic Eddies. Journal of Physical Oceanography, Vol. 48, Issue. 9, p. 1995.

    Núñez-Antonio, Gabriel Mendoza, Manuel Contreras-Cristán, Alberto Gutiérrez-Peña, Eduardo and Mendoza, Eduardo 2018. Bayesian nonparametric inference for the overlap of daily animal activity patterns. Environmental and Ecological Statistics, Vol. 25, Issue. 4, p. 471.

    CHINCO, ALEX CLARK-JOSEPH, ADAM D. and YE, MAO 2018. Sparse Signals in the Cross-Section of Returns. The Journal of Finance,

    Dionne-Odom, J. Nicholas Applebaum, Allison J. Ornstein, Katherine A. Azuero, Andres Warren, Paula P. Taylor, Richard A. Rocque, Gabrielle B. Kvale, Elizabeth A. Demark-Wahnefried, Wendy Pisu, Maria Partridge, Edward E. Martin, Michelle Y. and Bakitas, Marie A. 2018. Participation and interest in support services among family caregivers of older adults with cancer. Psycho-Oncology, Vol. 27, Issue. 3, p. 969.

    Du, Jiang Cao, Ruiyuan Kwessi, Eddy and Zhang, Zhongzhan 2018. Estimation for generalized partially functional linear additive regression model. Journal of Applied Statistics, p. 1.

    Narwaria, Manish 2018. Toward Better Statistical Validation of Machine Learning-Based Multimedia Quality Estimators. IEEE Transactions on Broadcasting, Vol. 64, Issue. 2, p. 446.

    2018. Linear Models and Time-Series Analysis. p. 825.

    Dionne-Odom, J. Nicholas Ejem, Deborah Azuero, Andres Taylor, Richard A. Rocque, Gabrielle B. Turkman, Yasemin Thompson, Moneka A. Knight, Sara J. Martin, Michelle Y. and Bakitas, Marie A. 2018. Factors Associated with Family Caregivers' Confidence in Future Surrogate Decision Making for Persons with Cancer. Journal of Palliative Medicine, Vol. 21, Issue. 12, p. 1705.

    ×

Book description

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.

Reviews

‘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

‘Absolutely brilliant. This beautifully written compendium reviews many big statistical ideas, including the authors' own. A must for anyone engaged creatively in statistics and the data sciences, for repeated use. Efron and Hastie demonstrate the ever-growing power of statistical reasoning, past, present, and future.’

Carl Morris - Harvard University, Massachusetts

'Computer Age Statistical Inference gives a lucid guide to modern statistical inference for estimation, hypothesis testing, and prediction. The book seamlessly integrates statistical thinking with computational thinking, while covering a broad range of powerful algorithms for learning from data. It is extraordinarily rare and valuable to have such a unified treatment of classical (and classic) statistical ideas and recent 'big data' and machine learning ideas. Accessible real-world examples and insightful remarks can be found throughout the book.'

Joseph K. Blitzstein - Harvard University, Massachusetts

'Among other things, it is an attempt to characterize the current state of statistics by identifying important tools in the context of their historical development. It also offers an enlightening series of illustrations of the interplay between computation and inference … This is an attractive book that invites browsing by anyone interested in statistics and its future directions.'

Bill Satzer Source: Mathematical Association of America Reviews

'My take on Computer Age Statistical Inference is that experienced statisticians will find it helpful to have such a compact summary of twentieth-century statistics, even if they occasionally disagree with the book’s emphasis; students beginning the study of statistics will value the book as a guide to statistical inference that may offset the dangerously mind-numbing experience offered by most introductory statistics textbooks; and the rest of us non-experts interested in the details will enjoy hundreds of hours of pleasurable reading.'

Joseph Rickert Source: RStudio (www.rstudio.com)

'Efron and Hastie (both, Stanford Univ.) have superbly crafted a central text/reference book that presents a broad overview of modern statistics. The work examines major developments in computation from the late-20th and early-21st centuries, ranging from electronic computations to 'big data' analysis. 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.'

D. J. Gougeon Source: Choice

Refine List
Actions for selected content:
Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Send to Kindle
  • Send to Dropbox
  • Send to Google Drive
  • Send content to

    To send content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about sending content to .

    To send content items to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle.

    Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

    Find out more about the Kindle Personal Document Service.

    Please be advised that item(s) you selected are not available.
    You are about to send
    ×

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
×

Page 1 of 2



Page 1 of 2


Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Book summary page views

Total views: 0 *
Loading metrics...

* Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.

Usage data cannot currently be displayed