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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Proceedings ArticleDOI
Online semi-parametric learning for inverse dynamics modeling
TL;DR: In this article, a semi-parametric algorithm for online learning of a robot inverse dynamics model is presented, which combines the strength of the parametric and nonparametric modeling.
Journal ArticleDOI
A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5 concentration
Veronica J. Berrocal,Yawen Guan,Amanda Muyskens,Haoyu Wang,Brian J. Reich,James A. Mulholland,Howard H. Chang +6 more
TL;DR: In this article, a variety of exposure modeling approaches, such as ordinary least squares and inverse distance weighting, kriging, statistical downscaling models, and land use regression are compared.
Journal ArticleDOI
Structure-function multi-scale connectomics reveals a major role of the fronto-striato-thalamic circuit in brain aging
Paolo Bonifazi,Asier Erramuzpe,Ibai Diez,Iñigo Gabilondo,Matthieu P. Boisgontier,Lisa Pauwels,Sebastiano Stramaglia,Stephan P. Swinnen,Jesus M. Cortes,Jesus M. Cortes +9 more
TL;DR: It is found that aging widely affects the connectivity of multiple structures, and the connectivity between basal ganglia and thalamus to frontal areas, also known as the fronto‐striato‐thalamic (FST) circuit, makes the major contribution to age estimation.
Journal ArticleDOI
The socio-demographic and geographical dimensions of fuel poverty in Italy
Giorgio Besagni,Marco Borgarello +1 more
TL;DR: In this article, the authors proposed a measure of fuel poverty that considers details on household energy expenditure and its impacts on aggregated regional and national levels based on an integrated statistical-lumped parameter approach.