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The book is organized into three parts. 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. This doesn’t mean that every advance was computer-related. © 2016 - 2020 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. 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. Then they raise issues and contrast and compare the merits of each approach. Computer Age Statistical Inference contains no code, but it is clearly an R-informed text with several plots and illustrations. 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. His key data analytic methods … were almost always applied frequentistically. Finally, for those of you who won’t buy a book without thumbing through it, PD Dr. Pablo Emilio Verde has you covered. In 475 carefully crafted pages, Efron and Hastie examine the last 100 years or so of statistical thinking from multiple viewpoints. 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. Their Fisherian rationale, however, often drew on ideas neither Bayesian nor frequentist in nature, or sometimes the two in combination. “Part II: Early Computer-Age Methods” has nine chapters on Empirical Bayes, James-Stein Estimation and Ridge Regression, Generalized Linear Models and Regression Trees, Survival Analysis and the EM Algorithm, The Jackknife and the Bootstrap, Bootstrap Confidence Intervals, Cross-Validation and Cp Estimates of Prediction Error, Objective Bayes Inference and MCMC, and Postwar Statistical Inference and Methodology. 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 by Efron and Hastie is a great overview of algorithms and statistical techniques used in machine learning :) His key data analytic methods … were almost always applied frequentistically. 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. On the first page of the preface they write: … the role of electronic computation is central to our story. Then they raise issues and contrast and compare the merits of each approach. Above all, in this text, Efron and Hastie are concerned with the clarity of statistical inference. “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. Their Fisherian rationale, however, often drew on ideas neither Bayesian nor frequentist in nature, or sometimes the two in combination. 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. 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. 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. “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 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. A land bridge had opened up to a new continent but not all were eager to cross. 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. From the first page, they maintain a unified exposition of their material by presenting statistics as a tension between algorithms and inference. Posted on October 28, 2016 by [email protected] in R bloggers | 0 Comments. Nothing Efron and Hastie do throughout this entire trip is pedestrian. In these, they invite the reader to consider a familiar technique from either a Bayesian, Frequentist or Fisherian point of view. “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. 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. A second path opened up in this text stops just short of the high ground of philosophy. 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. 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. In these, they invite the reader to consider a familiar technique from either a Bayesian, Frequentist or Fisherian point of view. 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). A great pedagogical strength of the book is the “Notes and Details” section concluding each chapter. The book is organized into three parts.
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