An Interview with Bradley Efron and Trevor Hastie, authors of Computer Age Statistical Inference
16.6 هزار بار بازدید -
7 سال پیش
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Bradley Efron is Max H.
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 worked extensively on theories of statistical inference, and is the inventor of the bootstrap sampling technique. Along with Sir David Cox, he is the winner of the first International Prize in Statistics. He also 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.
Winner of the 2017 PROSE Award for Computing and Information Sciences, Computer Age Statistical Analysis 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.
Find out more at www.cambridge.org/CASI
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.
Winner of the 2017 PROSE Award for Computing and Information Sciences, Computer Age Statistical Analysis 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.
Find out more at www.cambridge.org/CASI
7 سال پیش
در تاریخ 1396/05/25 منتشر شده
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