BookDOI
An introduction to statistical learning
TLDR
An introduction to statistical learning provides an accessible overview of the essential toolset for making sense of the vast and complex data sets that have emerged in science, industry, and other sectors in the past twenty years.Abstract:
Statistics An Intduction to Stistical Lerning with Applications in R An Introduction to Statistical Learning provides an accessible overview of the fi eld of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fi elds ranging from biology to fi nance to marketing to astrophysics in the past twenty years. Th is book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classifi cation, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fi elds, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical soft ware platform. Two of the authors co-wrote Th e Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. Th is book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. Th e text assumes only a previous course in linear regression and no knowledge of matrix algebra.read more
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Statistical Learning Methods Applied to Process Monitoring: An Overview and Perspective
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TL;DR: An overview of the core areas of data science are presented, application areas from within chemical engineering research are discussed, and perspectives on how data science principles can be included in the authors' training are concluded.
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From Isolation to Radicalization: Anti-Muslim Hostility and Support for ISIS in the West
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Technical Note—A Robust Perspective on Transaction Costs in Portfolio Optimization
TL;DR: The empirical results demonstrate that the data-driven portfolios perform favorably because they strike an optimal trade-off between rebalancing the portfolio to capture the information in recent historical return data and avoiding the large transaction costs and impact of estimation error associated with excessive trading.
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Voice patterns in schizophrenia: A systematic review and Bayesian meta-analysis.
TL;DR: Studies of acoustic patterns are a promising but, yet unsystematic avenue for establishing markers of schizophrenia, and recommendations towards more cumulative, open, and theory-driven research are outlined.