Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs, Series Number 6). The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. "Data science" and "machine learning" have become familiar terms in the news as statistical methods are applied to the huge datasets of modern science and commerce. How did we get here? And where are we going? How does it all fit together?
Now in a paperback edition and enriched with exercises, this book offers a course focused on modern statistical thinking. Starting from classical inferential theories - Bayesian, frequentist, fisherwoman - the individual chapters address a series of influential themes: survival analysis, logistic regression, empirical Bayes, jackknife and bootstrap, random forests, neural networks, Monte Carlo Markov chain , inference after model selection and dozens of others. The decidedly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises and the book ends with speculations about the future direction of statistics and data science.
Bradley Efron is Professor of Max H. Stein, Professor of Statistics and Professor of Biomedical Data Sciences at Stanford University. He has made faculty visits at Harvard, the University of California, Berkeley, and Imperial College London. Efron has worked extensively on theories of statistical inference and is the inventor of the bootstrap sampling technique. He was awarded the National Medal of Science in 2005, the Gold Guy Medal of the Royal Statistical Society in 2014 and the International Statistical Award in 2019.
Trevor Hastie is Professor John A. Overdeck, Professor of Statistics and Professor of Biomedical Sciences at Stanford University. He is the co-author of The Elements of Statistical Learning (2009), a key text on modern data analysis. He is also known for his work on generalized additive models and for his contributions to the computing environment of the language R. Hastie was elected to the National Academy of Sciences in 2018, received the Sigillum Magnum of the University of Bologna in 2019, and Leo Breiman from the American Statistical Association in 2020 r.
This book does what other statistics and ML textbooks do not: guide the motivated reader through the field and speak to them as if it were their own.
I was familiar with the earlier texts of Dr. Hastie (they are somewhat canonical in this area), less those of Dr. Efron. I think Dr. Efron's lively writing style is an important part. For my money, this book far outperforms previous books written by Dr. Hastie - it's the most readable, most integrated, and most widely read.
Note that I do not include people with no prior mathematical knowledge in the “wider audience” category. On the contrary, I think this book will be very useful for data scientists, ML engineers or anyone who uses statistical inference in their work. This is a serious textbook intended for undergraduate or graduate first year students. Student in statistics. It doesn't check the basics of probability (and is a better book for that). Even so, it is accessible and has a nice way of being near the bed.
I can't say enough good things about the spelling in this book. I have read a lot of math textbooks and the presentation here is just great. Words are chosen with care: for example, the words "machine learning" are not overused (note their absence in the title) which, in my opinion, is a deliberate attempt to keep the material in statistical theory. and remove it from the moss, which is ubiquitous on the ground. The presentation is also not overloaded by the reflexive reuse of the words "model" or "parameters" or "data". Efron and Hastie use concrete and lively language.
I also recommend Drs Efron and Hastie for their great gift of making the text available online for free. What a contribution to the subject.
Topics on Big Data are growing rapidly. From the first 3 V’s that originally characterized Big Data, the industry now has identified 42 V’s associated with Big Data. The list of how we characteriz...
Acquire and analyze data from all corners of the social web with Python. This book is for intermediate Python developers who want to engage with the use of public APIs to collect data from social m...
Learn how data literacy is changing the world and giving you a better understanding of life's biggest problems in this "Important and Comprehensive" Guide to Statistical Thinking (New York). The bi...
The rate at which we produce data is growing steadily, thus creating even larger streams of continuously evolving data. Online news, micro-blogs, search queries are just a few examples of these contin...