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You may leave a comment below or discuss the post in the forum community.rstudio.com. You may have thought that Sir Ronald Fisher was a frequentist, but the inspired thoughts of a man of Fisher’s intellect are not so easily categorized. Finally, for those of you who won’t buy a book without thumbing through it, PD Dr. Pablo Emilio Verde has you covered. With this in mind, it seems plausible that there really isn’t any big disconnect between the strict logic required to think your way through the pitfalls of large-scale hypothesis testing, and the almost cavalier application of machine learning models. In the nominal approach implied by the book’s title, they describe the impact of computing on statistics, and point out where powerful computers opened up new territory. Computer Age Statistical Inference by Efron and Hastie is a great overview of algorithms and statistical techniques used in machine learning :) This doesn’t mean that every advance was computer-related. Datasets used in CASI. Computer Age Statistical Inference: Algorithms, Evidence and Data Science by Bradley Efron and Trevor Hastie is a brilliant read. You may have thought that Sir Ronald Fisher was a frequentist, but the inspired thoughts of a man of Fisher’s intellect are not so easily categorized. On the first page of the preface they write: … the role of electronic computation is central to our story. Case-Control Studies, by Ruth H. Keogh and D. R. Cox 5. The data sets provided on Efron’s website, and the pseudo-code placed throughout the text are helpful for replicating much of what is described. Efron and Hastie blow by the great divide of the Bayesian versus Frequentist controversy to carefully consider the strengths and weaknesses of the three main systems of statistical inference: Frequentist, Bayesian and Fisherian Inference. With this in mind, it seems plausible that there really isn’t any big disconnect between the strict logic required to think your way through the pitfalls of large-scale hypothesis testing, and the almost cavalier application of machine learning models. Instead, they start with a Poisson family example, deriving a 2 parameter general expression for the family and showing how “tilting” the distribution by multiplying by an exponential parameter permits the derivation of other members of the family. Their Fisherian rationale, however, often drew on ideas neither Bayesian nor frequentist in nature, or sometimes the two in combination. 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. A great pedagogical strength of the book is the “Notes and Details” section concluding each chapter. The Epilogue ties everything together with a historical perspective that outlines how the focus of statistical progress has shifted between Applications, Mathematics and Computation throughout the twentieth century and the early part of this century. For example, their approach to the exponential family of distributions underlying generalized linear models doesn’t begin with the usual explanation of link functions fitting into the standard exponential family formula. In the nominal approach implied by the book’s title, they describe the impact of computing on statistics, and point out where powerful computers opened up new territory. Here you will find derivation details, explanations of Frequentist, Bayesian and Fisherian inference, and remarks of historical significance. Nothing Efron and Hastie do throughout this entire trip is pedestrian. His key data analytic methods … were almost always applied frequentistically. Nothing Efron and Hastie do throughout this entire trip is pedestrian. All Rights Reserved. “Part I: Classic Statistical Inference” contains five chapters on classical statistical inference, including a gentle introduction to algorithms and inference, three chapters on the inference systems mentioned above, and a chapter on parametric models and exponential families. Efron and Hastie will keep your feet firmly on the ground while they walk you slowly through the details, pointing out what is important, and providing the guidance necessary to keep the whole forest in mind while studying the trees. The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. The data sets provided on Efron’s website , and the pseudo-code placed throughout the text are helpful for replicating much of what is described. This doesn’t mean that every advance was computer-related. In these, they invite the reader to consider a familiar technique from either a Bayesian, Frequentist or Fisherian point of view. A great pedagogical strength of the book is the “Notes and Details” section concluding each chapter. Have a look at the table of contents. Computer Age Statistical Inference code for textbook - optixlab/CASI Their Fisherian rationale, however, often drew on ideas neither Bayesian nor frequentist in nature, or sometimes the two in combination. Copyright © 2020 | MH Corporate basic by MH Themes, Click here if you're looking to post or find an R/data-science job, Introducing our new book, Tidy Modeling with R, How to Explore Data: {DataExplorer} Package, R – Sorting a data frame by the contents of a column, Multi-Armed Bandit with Thompson Sampling, 100 Time Series Data Mining Questions – Part 4, Whose dream is this? Computer Age Statistical Inference, by Bradley Efron and Trevor Hastie. The data sets provided on Efron’s website, and the pseudo-code placed throughout the text are helpful for replicating much of what is described. The website points to the boot and bootstrap packages, and provides the code for a function used in the notes to the chapter on bootstrap confidence intervals. Computer Age Statistical Inference Algorithms, Evidence, and Data Science BRADLEY EFRON Stanford University, California TREVOR HASTIE Stanford University, California . D&D’s Data Science Platform (DSP) – making healthcare analytics easier, High School Swimming State-Off Tournament Championship California (1) vs. Texas (2), Junior Data Scientist / Quantitative economist, Data Scientist – CGIAR Excellence in Agronomy (Ref No: DDG-R4D/DS/1/CG/EA/06/20), Data Analytics Auditor, Future of Audit Lead @ London or Newcastle, python-bloggers.com (python/data-science news), Python Musings #4: Why you shouldn’t use Google Forms for getting Data- Simulating Spam Attacks with Selenium, Building a Chatbot with Google DialogFlow, LanguageTool: Grammar and Spell Checker in Python, Click here to close (This popup will not appear again). The Epilogue ties everything together with a historical perspective that outlines how the focus of statistical progress has shifted between Applications, Mathematics and Computation throughout the twentieth century and the early part of this century. Computer Age Statistical Inference contains no code, but it is clearly an R-informed text with several plots and illustrations. “Part III: Twenty-First-Century Topics” dives into the details of large-scale inference and data science, with seven chapters on Large-Scale Hypothesis Testing, Sparse Modeling and the Lasso, Random Forests and Boosting, Neural Networks and Deep Learning, Support Vector Machines and Kernel methods, Inference After Model Selection, and Empirical Bayes Estimation Strategies. Empirical Bayes and James-Stein estimation, they claim, could have been discovered under the constraints of mid-twentieth-century mechanical computation, but discovering the bootstrap, proportional hazard models, large-scale hypothesis testing, and the machine learning algorithms underlying much of data science required crossing the bridge. A land bridge had opened up to a new continent but not all were eager to cross. Computer Age Statistical Inference: Algorithms, Evidence and Data Science. For example, their approach to the exponential family of distributions underlying generalized linear models doesn’t begin with the usual explanation of link functions fitting into the standard exponential family formula. A land bridge had opened up to a new continent but not all were eager to cross. When and how to use the Keras Functional API, Moving on as Head of Solutions and AI at Draper and Dash. Then they raise issues and contrast and compare the merits of each approach. But don’t let me mislead you into thinking that Computer Age Statistical Inference is mere philosophical fluff that doesn’t really matter day-to-day. Efron and Hastie write: Sir Ronald Fisher was arguably the most influential anti-Bayesian of all time, but that did not make him a conventional frequentist. A second path opened up in this text stops just short of the high ground of philosophy. “Part III: Twenty-First-Century Topics” dives into the details of large-scale inference and data science, with seven chapters on Large-Scale Hypothesis Testing, Sparse Modeling and the Lasso, Random Forests and Boosting, Neural Networks and Deep Learning, Support Vector Machines and Kernel methods, Inference After Model Selection, and Empirical Bayes Estimation Strategies. Have a look at the table of contents. Naive Bayes: A Generative Model and Big Data Classifier. On the first page of the preface they write: … the role of electronic computation is central to our story. © Computer Age Statistical Inference 2016 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. A second path opened up in this text stops just short of the high ground of philosophy. Computer Age Statistical Inference contains no code, but it is clearly an R-informed text with several plots and illustrations. “Part I: Classic Statistical Inference” contains five chapters on classical statistical inference, including a gentle introduction to algorithms and inference, three chapters on the inference systems mentioned above, and a chapter on parametric models and exponential families. The data sets provided on Efron’s website , and the pseudo-code placed throughout the text are helpful for replicating much of what is described. Then they raise issues and contrast and compare the merits of each approach. Unstated, but nagging in the back of my mind while reading these chapters, was the implication that there may, indeed, be other paths to the “science of learning from experience” (the authors’ definition of statistics) that have yet to be discovered. 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