computer age statistical inference r code

They take special care to explain the nuances of Frequentist, Bayesian and Fisherian thinking, devoting early chapters to each of these conceptual frameworks. 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. 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 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. 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. The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. In these, they invite the reader to consider a familiar technique from either a Bayesian, Frequentist or Fisherian point of view. 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. Finally, for those of you who won’t buy a book without thumbing through it, PD Dr. Pablo Emilio Verde has you covered. His key data analytic methods … were almost always applied frequentistically. 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. 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 Bradley Efron and Trevor Hastie. Naive Bayes: A Generative Model and Big Data Classifier. “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. Nothing Efron and Hastie do throughout this entire trip is pedestrian. 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. Have a look at the table of contents. 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. R – Risk and Compliance Survey: we need your help! Then they raise issues and contrast and compare the merits of each approach. The book is organized into three parts. 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. 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. They take special care to explain the nuances of Frequentist, Bayesian and Fisherian thinking, devoting early chapters to each of these conceptual frameworks. 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. A land bridge had opened up to a new continent but not all were eager to cross. “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. In 475 carefully crafted pages, Efron and Hastie examine the last 100 years or so of statistical thinking from multiple viewpoints. A great pedagogical strength of the book is the “Notes and Details” section concluding each chapter. You may leave a comment below or discuss the post in the forum community.rstudio.com. On the first page of the preface they write: … the role of electronic computation is central to our story. 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. 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.

Vermintide 2 Fanatic, Garment Bag Mockup, Lamy Aion Review, Calvin Klein Person, Illinois College Mascot Blueboys, Accounting Firms Calgary, Tone City Sweet Cream, Baldwin High School Shaun Tomaszewski,

Похожие записи

  • Нет похожих записей
вверх

Добавить комментарий

Ваш e-mail не будет опубликован. Обязательные поля помечены *