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[Bradley Efron, Trevor Hastie] ✓ Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs) õ Read Online eBook or Kindle ePUB. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs) Interesting, but too much overlap with their other books Their previous books include:An Introduction to the Bootstrap (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction (Institute of Mathematical Statistics Monographs)The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)Statistical Learning with Sparsity: The Lasso and Gene
| Title | : | Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs) |
| Author | : | |
| Rating | : | 4.96 (516 Votes) |
| Asin | : | 1107149894 |
| Format Type | : | paperback |
| Number of Pages | : | 491 Pages |
| Publish Date | : | 2016-12-08 |
| Language | : | English |
. He received the National Medal of Science in 2005 and the Guy Medal in Gold of the Royal Statistical Society in 2014.Trevor Hastie is John A. He is coauthor of Elements of Statistical Learning, a key text in the field of modern data analysis. He is also known for his work on generalized additive models and principal curves, and for his contributions to the R computing environment. Overdeck Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University, Ca
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. '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. The distinctly modern approach integrates methodology and algorithms with statistical inference. The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. The book ends with speculation on the future direction of statistics and data science.
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. 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. Blitzstein, Harvard University, Massachusetts . This beautifully written compendium reviews many big statistical ideas, including the authors' own. The authors' perspective is summarized nicely when they say, 'very roughly speaking, algorithms are what statisticians do, while inference says why they do them'. Whatever the outcome, the authors provide a vision of high-speed computing having tremendous potential to enable the contr
Interesting, but too much overlap with their other books Their previous books include:An Introduction to the Bootstrap (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction (Institute of Mathematical Statistics Monographs)The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC M
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