本書以豐富的案例介紹了計(jì)算機(jī)時代下的統(tǒng)計(jì)推斷的發(fā)展脈絡(luò)，從理論的角度剖析統(tǒng)計(jì)推斷的各類算法、證據(jù)等，揭示統(tǒng)計(jì)推斷如何推動當(dāng)今大數(shù)據(jù)、數(shù)據(jù)科學(xué)、機(jī)器學(xué)習(xí)等領(lǐng)域的快速發(fā)展并引領(lǐng)數(shù)據(jù)分析的變革，最后展望了統(tǒng)計(jì)學(xué)和數(shù)據(jù)科學(xué)的未來方向。

Bradley Efron is Max H. Stein Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University, California. He has held visiting faculty appointments at Harvard University, Massachusetts, the University of California, Berkeley, and Imperial College of Science, Technology and Medicine, London. Efron has worked extensively on theories of statist...

Bradley Efron is Max H. Stein Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University, California. He has held visiting faculty appointments at Harvard University, Massachusetts, the University of California, Berkeley, and Imperial College of Science, Technology and Medicine, London. Efron has worked extensively on theories of statistical inference, and is the inventor of the bootstrap sampling technique. 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. Overdeck Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University, California. 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. Hastie was awarded the Emmanuel and Carol Parzen prize for Statistical Innovation in 2014.