Matrix-Based Introduction to Multivariate Data Analysis
- ISBN-10
- 9811023417
- ISBN-13
- 9789811023415
- Category
- Mathematics
- Pages
- 301
- Language
- English
- Published
- 2016-10-11
- Publisher
- Springer
- Author
- Kohei Adachi
Description
This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter. This volume is appropriate for undergraduate students who already have studied introductory statistics. Graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis will also find the book useful, as it is based on modern matrix formulations with a special emphasis on singular value decomposition among theorems in matrix algebra. The book begins with an explanation of fundamental matrix operations and the matrix expressions of elementary statistics, followed by the introduction of popular multivariate procedures with advancing levels of matrix algebra chapter by chapter. This organization of the book allows readers without knowledge of matrices to deepen their understanding of multivariate data analysis.
Get the book
Other editions
Similar books
-
Multivariate Data Analysis: In Practice : an Introduction to Multivariate Data Analysis and Experimental Design
By Kim H. Esbensen, Dominique Guyot, Frank WestadThe book's principal objective is to provide a conceptual framework for multivariate data analysis techniques, enabling the reader to apply these in his or her own field.
-
Multivariate Data Analysis on Matrix Manifolds: (with Manopt)
By Nickolay Trendafilov, Michele GalloThe book includes numerous in-text examples as well as Manopt codes and software guides, which can be applied directly or used as templates for solving similar and new problems.
-
Introduction to Multivariate Analysis
By Chris ChatfieldThis book provides an introduction to the analysis of multivariate data.It describes multivariate probability distributions, the preliminary analysisof a large -scale set of data, princ iple component and factor analysis,traditional normal ...
-
Multivariate Data Analysis: An Introduction
By Barbara BundMultivariate Data Analysis: An Introduction
-
Multivariate Data Analysis
By Joseph F. HairKEY BENEFIT: For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. Hair, et. al provides an applications-oriented introduction...
-
Multivariate Data Analysis: An Introduction to Multivariate Analysis, Process Analytical Technology and Quality by Design
By Patrick J. Whitcomb, Mark J. Anderson, Kim H. EsbensenThis long-anticipated textbook is currently the most up-to-date resource on chemometrics and Multivariate Data Analysis (MVA).
-
An Introduction to Multivariate Statistical Analysis
By Theodore W. AndersonMultivariate Statistical Simulation Mark E. Johnson For the researcher in statistics, probability, and operations research involved in the design and execution of a computer-aided simulation study utilizing continuous multivariate distributions,...
-
Methods of Multivariate Analysis
By Alvin C. RencherThis textbook is likely to become a useful reference for students in their future work." —Journal of the American Statistical Association "In this well-written and interesting book, Rencher has done a great job in presenting intuitive and ...
-
Multivariate Data Analysis with Readings
By Joseph F. HairDesigned for the non-statistician, this applications-oriented introduction to multivariate analysis greatly reduces the amount of statistical notation and terminology used, while focusing instead on the fundamental concepts that affect the...
-
An Introduction to Applied Multivariate Analysis with R
By Brian Everitt, Torsten HothornWe first find the convex hull of the data (i.e., the observations defining the convex hull) using the following R code: R> (hull with(USairpollution, ...