Download or read book Object Oriented Data Analysis written by J. S.. DRYDEN MARRON (IAN L.) and published by CRC Press. This book was released on 2021-11-08 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object Oriented Data Analysis is a framework that facilitates inter-disciplinary research through new terminology for discussing the often many possible approaches to the analysis of complex data. Such data are naturally arising in a wide variety of areas. This book aims to provide ways of thinking that enable the making of sensible choices. The main points are illustrated with many real data examples, based on the authors' personal experiences, which have motivated the invention of a wide array of analytic methods. While the mathematics go far beyond the usual in statistics (including differential geometry and even topology), the book is aimed at accessibility by graduate students. There is deliberate focus on ideas over mathematical formulas. J. S. Marron is the Amos Hawley Distinguished Professor of Statistics, Professor of Biostatistics, Adjunct Professor of Computer Science, Faculty Member of the Bioinformatics and Computational Biology Curriculum and Research Member of the Lineberger Cancer Center and the Computational Medicine Program, at the University of North Carolina, Chapel Hill. Ian L. Dryden is Professor of Statistics in the School of Mathematical Sciences at the University of Nottingham, has served as Head of School, and is joint author of the acclaimed book Statistical Shape Analysis. "Lots of common-sense advice, a lot of informative graphs, and not too much theory...A breath of fresh air in an area where there can be a tendency to make the material overly technical." (John Kent, University of Leeds) "We need this book badly in statistics." (Jim Ramsay, McGill University) "The particular strength of this book is that is connects classical statistics, "classical" machine learning and statistics on non-Euclidean spaces with one another...This is a book I have been waiting for." (Stephan F. Huckemann, Georgia-Augusta-University Goettingen) "An exciting and timely project highlighting the importance of non-Euclidean data across different scientific applications...Covers important and fast-developing topic areas that are important in many applications." (Anuj Srivastava, Florida State University)