# computer age statistical inference r code

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 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. Datasets used in CASI. Naive Bayes: A Generative Model and Big Data Classifier. Their Fisherian rationale, however, often drew on ideas neither Bayesian nor frequentist in nature, or sometimes the two in combination. 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 write: Sir Ronald Fisher was arguably the most influential anti-Bayesian of all time, but that did not make him a conventional frequentist. A land bridge had opened up to a new continent but not all were eager to cross. Computer Age Statistical Inference code for textbook - optixlab/CASI The example is interesting in its own right, but the payoff, which comes a couple of pages later, is argument demonstrating how a generalization of the technique keeps the number of parameters required for inference under repeated sampling from growing without bound. The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. Here you will find derivation details, explanations of Frequentist, Bayesian and Fisherian inference, and remarks of historical significance. 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. 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. 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. 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. Then they raise issues and contrast and compare the merits of each approach. On the first page of the preface they write: … the role of electronic computation is central to our story. 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. Computer Age Statistical Inference by Efron and Hastie is a great overview of algorithms and statistical techniques used in machine learning :) A second path opened up in this text stops just short of the high ground of philosophy. In these, they invite the reader to consider a familiar technique from either a Bayesian, Frequentist or Fisherian point of view. RStudio, PBC. Computer Age Statistical Inference contains no code, but it is clearly an R-informed text with several plots and illustrations. This doesn’t mean that every advance was computer-related. The book is organized into three parts. 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. A great pedagogical strength of the book is the “Notes and Details” section concluding each chapter. From the first page, they maintain a unified exposition of their material by presenting statistics as a tension between algorithms and inference. Computer Age Statistical Inference contains no code, but it is clearly an R-informed text with several plots and illustrations. 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. 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. 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. “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. 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? 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. R – Risk and Compliance Survey: we need your help! From the first page, they maintain a unified exposition of their material by presenting statistics as a tension between algorithms and inference. 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. 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. In 475 carefully crafted pages, Efron and Hastie examine the last 100 years or so of statistical thinking from multiple viewpoints. Nothing Efron and Hastie do throughout this entire trip is pedestrian. “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. Computer Age Statistical Inference: Algorithms, Evidence and Data Science. 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. Above all, in this text, Efron and Hastie are concerned with the clarity of statistical inference. His key data analytic methods … were almost always applied frequentistically. 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. Computer Age Statistical Inference: Algorithms, Evidence and Data Science by Bradley Efron and Trevor Hastie is a brilliant read. Computer Age Statistical Inference, by Bradley Efron and Trevor Hastie. 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. Then they raise issues and contrast and compare the merits of each approach. Here you will find derivation details, explanations of Frequentist, Bayesian and Fisherian inference, and remarks of historical significance. 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. 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.

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