Statistical Graphics for Univariate and Bivariate Data: William G. Jacoby
Using R and RStudio for Data Management, Statistical Analysis, and Graphics: Nicholas J. Horton, Ken Kleinman
Presents a unique foundation for producing almost every quantitative graphic found in scientific journals, newspapers, statistical packages, and data visualization systems The new edition features six new chapters and has undergone substantial revision. The first edition has sold more than 2200 copies. Four color throughout.
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book´s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for ´´wide´´ data (p bigger than n), including multiple testing and false discovery rates.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, 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 fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The 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. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
´´Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, alongwith solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), the book is ideal for research scientists, final-year undergraduate or graduate-level students of applied statistics, and practising statisticians. It is both for learning and for reference. This third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests´´--Provided by publisher.
Statistical Atlases and Computational Models of the Heart:First International Workshop, STACOM 2010, and Cardiac Electrophysical Simulation Challenge, CESC 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010, Proceedings Lecture Notes in Computer Science Image Processing, Computer Vision, Pattern Recognition, and Graphics. Auflage 2010
Structural, Syntactic, and Statistical Pattern Recognition:Joint IAPR International Workshop, SSPR & SPR 2012, Hiroshima, Japan, November 7-9, 2012, Proceedings Lecture Notes in Computer Science Image Processing, Computer Vision, Pattern Recognition, and Graphics. Auflage 2012
Structural, Syntactic, and Statistical Pattern Recognition:Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings Lecture Notes in Computer Science Image Processing, Computer Vision, Pattern Recognition, and Graphics. Auflage 2008