The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. '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. How did we get here? And where are we going? This book takes u The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. '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. 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. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Computer Age Statistical Inference: Algorithms, Evidence, and Data Science
The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. '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. How did we get here? And where are we going? This book takes u The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. '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. 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. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
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Adam Zabell –
tl;dr - medium grain statistical review from Classic (frequentist, Bayesian) to Modern (Monte Carlo, support vector machines) I may never actually finish reading this book. That's okay because I don't think this is the kind of book you just read. I and several folk I work with are making our way through it, using data from their website with the figures as targeted examples for our R scripts. It's a phenominal resource for showing the flow of thinking about statistics, from the 'strictly pen and tl;dr - medium grain statistical review from Classic (frequentist, Bayesian) to Modern (Monte Carlo, support vector machines) I may never actually finish reading this book. That's okay because I don't think this is the kind of book you just read. I and several folk I work with are making our way through it, using data from their website with the figures as targeted examples for our R scripts. It's a phenominal resource for showing the flow of thinking about statistics, from the 'strictly pen and paper' needs through to the 'big data' prospecting being done today. I may change my mind as we continue, but for the moment I'm looking at this book as a reasonable survey of possible techniques to solve possible problems. When a new problem comes up, I can reach back to this book to help decide the best approach, and then dig deeper and elsewhere to make sure none of the typos (this is the first edition, of course there are typos) disrupt my solution.
Gourav Sengupta –
Brings forward an amazing clarity, its an experience worth going through. (Of course, given that I am an extremely mediocre person, using Google helped me get the most out of this book).
Xiao Xiao –
An awesome book that connects multiple branches of statistics, from the root of mathematical stats all the way to the tip of data science. It's on my list for 2nd-reads so that I could try to follow the equations and derivations this time (though without my 20-year-old brain it's gonna take some time). An awesome book that connects multiple branches of statistics, from the root of mathematical stats all the way to the tip of data science. It's on my list for 2nd-reads so that I could try to follow the equations and derivations this time (though without my 20-year-old brain it's gonna take some time).
Michael Downs –
Quite good.
Terran M –
This is a well written, beautiful book that I enjoyed reading very much. Its best use is to tie together disparate concepts that you are already familiar with. As a 450 page survey of 100+ years of statistics, it will not be accessible unless you already know about most of the topics it covers. If you have already read An Introduction to Statistical Learning, and Applied Predictive Modeling, and Kruschke or Gelman on Bayesian data analysis, and Benjamini and Hochberg's paper on FDR, then by all This is a well written, beautiful book that I enjoyed reading very much. Its best use is to tie together disparate concepts that you are already familiar with. As a 450 page survey of 100+ years of statistics, it will not be accessible unless you already know about most of the topics it covers. If you have already read An Introduction to Statistical Learning, and Applied Predictive Modeling, and Kruschke or Gelman on Bayesian data analysis, and Benjamini and Hochberg's paper on FDR, then by all means read this book. I suggest looking at the table of contents first, and proceeding only when all the chapter headings are familiar to you. The entire book is also available as a free PDF directly from the authors. I was disappointed to find that around chapter 16, the quality of the writing seemed to fall off, as if the last part of the book had been hurried. I nonetheless recommend it on the strength of the first 2/3.
Mark –
Two experts from Stanford have written a great historical/philosophical/mathematical overview of modern statistics that compares and contrasts frequentist, Bayesian and computer intensive algorithmic approaches to data analysis. I've fooled with all this stuff, but it's a pleasure having professors this smart tie it all together. It seems like it must have been an Herculean task. There are good examples and a very small amount of R code. The book, from Cambridge U press, is also very well produc Two experts from Stanford have written a great historical/philosophical/mathematical overview of modern statistics that compares and contrasts frequentist, Bayesian and computer intensive algorithmic approaches to data analysis. I've fooled with all this stuff, but it's a pleasure having professors this smart tie it all together. It seems like it must have been an Herculean task. There are good examples and a very small amount of R code. The book, from Cambridge U press, is also very well produced.
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Hu –