Matrix-Based Introduction to Multivariate Data Analysis

Matrix-Based Introduction to Multivariate Data Analysis
Author: Kohei Adachi
Publisher: Springer
Total Pages: 298
Release: 2016-10-11
Genre: Mathematics
ISBN: 9811023417

Download Matrix-Based Introduction to Multivariate Data Analysis Book in PDF, Epub and Kindle

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.

Multivariate Data Analysis

Multivariate Data Analysis
Author: Kim H. Esbensen
Publisher: Multivariate Data Analysis
Total Pages: 622
Release: 2002
Genre: Experimental design
ISBN: 9788299333030

Download Multivariate Data Analysis Book in PDF, Epub and Kindle

"Multivariate Data Analysis - in practice adopts a practical, non-mathematical approach to multivariate data analysis. The 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. Features: Focuses on the practical application of multivariate techniques such as PCA, PCR and PLS and experimental design. Non-mathematical approach - ideal for analysts with little or no background in statistics. Step by step introduction of new concepts and techniques promotes ease of learning. Theory supported by hands-on exercises based on real-world data. A full training copy of The Unscrambler (for Windows 95, Windows NT 3.51 or later versions) including data sets for the exercises is available. Tutorial exercises based on data from real-world applications are used throughout the book to illustrate the use of the techniques introduced, providing the reader with a working knowledge of modern multivariate data analysis and experimental design. All exercises use The Unscrambler, a de facto industry standard for multivariate data analysis software packages. Multivariate Data Analysis in Practice is an excellent self-study text for scientists, chemists and engineers from all disciplines (non-statisticians) wishing to exploit the power of practical multivariate methods. It is very suitable for teaching purposes at the introductory level, and it can always be supplemented with higher level theoretical literature."Résumé de l'éditeur.

Multivariate Data Analysis on Matrix Manifolds

Multivariate Data Analysis on Matrix Manifolds
Author: Nickolay Trendafilov
Publisher: Springer Nature
Total Pages: 467
Release: 2021-09-15
Genre: Mathematics
ISBN: 3030769747

Download Multivariate Data Analysis on Matrix Manifolds Book in PDF, Epub and Kindle

This graduate-level textbook aims to give a unified presentation and solution of several commonly used techniques for multivariate data analysis (MDA). Unlike similar texts, it treats the MDA problems as optimization problems on matrix manifolds defined by the MDA model parameters, allowing them to be solved using (free) optimization software Manopt. The 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. The first two chapters provide an overview and essential background for studying MDA, giving basic information and notations. Next, it considers several sets of matrices routinely used in MDA as parameter spaces, along with their basic topological properties. A brief introduction to matrix (Riemannian) manifolds and optimization methods on them with Manopt complete the MDA prerequisite. The remaining chapters study individual MDA techniques in depth. The number of exercises complement the main text with additional information and occasionally involve open and/or challenging research questions. Suitable fields include computational statistics, data analysis, data mining and data science, as well as theoretical computer science, machine learning and optimization. It is assumed that the readers have some familiarity with MDA and some experience with matrix analysis, computing, and optimization.

Introduction to Multivariate Analysis

Introduction to Multivariate Analysis
Author: Chris Chatfield
Publisher: CRC Press
Total Pages: 262
Release: 1981-05-15
Genre: Mathematics
ISBN: 9780412160400

Download Introduction to Multivariate Analysis Book in PDF, Epub and Kindle

This 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 theory material, as well as multidimensional scaling andcluster analysis.Introduction to Multivariate Analysis provides a reasonable blend oftheory and practice. Enough theory is given to introduce the concepts andto make the topics mathematically interesting. In addition the authors discussthe use (and misuse) of the techniques in pra ctice and present appropriatereal-life examples from a variety of areas includ ing agricultural research, soc iology and crim inology. The book should be suitable both for researchworkers and as a text for students taking a course on multivariate analysi

Introduction to Multivariate Analysis

Introduction to Multivariate Analysis
Author: Chris Chatfield
Publisher: Routledge
Total Pages: 197
Release: 2018-02-19
Genre: Mathematics
ISBN: 1351436783

Download Introduction to Multivariate Analysis Book in PDF, Epub and Kindle

This 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 theory material, as well as multidimensional scaling andcluster analysis.Introduction to Multivariate Analysis provides a reasonable blend oftheory and practice. Enough theory is given to introduce the concepts andto make the topics mathematically interesting. In addition the authors discussthe use (and misuse) of the techniques in pra ctice and present appropriatereal-life examples from a variety of areas includ ing agricultural research,soc iology and crim inology. The book should be suitable both for researchworkers and as a text for students taking a course on multivariate analysis.

An Introduction to Applied Multivariate Analysis with R

An Introduction to Applied Multivariate Analysis with R
Author: Brian Everitt
Publisher: Springer Science & Business Media
Total Pages: 284
Release: 2011-04-23
Genre: Mathematics
ISBN: 1441996508

Download An Introduction to Applied Multivariate Analysis with R Book in PDF, Epub and Kindle

The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.

Multivariate Data Analysis

Multivariate Data Analysis
Author: Barbara Bund
Publisher: McGraw-Hill/Irwin
Total Pages: 268
Release: 1983
Genre: Mathematics
ISBN:

Download Multivariate Data Analysis Book in PDF, Epub and Kindle

An Introduction to Multivariate Statistical Analysis

An Introduction to Multivariate Statistical Analysis
Author: Theodore W. Anderson
Publisher:
Total Pages: 720
Release: 1984-09-28
Genre: Mathematics
ISBN:

Download An Introduction to Multivariate Statistical Analysis Book in PDF, Epub and Kindle

1. Introduction; 2. The multivariate normal distribution; 3. Estimation of the mean vector and the covariance matrix; 4. Distributions and uses of sample correlation coefficients; 5. The generalized T2-Statistic; 6. Classification of observations; 7. The distribution of the sample covariance matrix and the sample generalized variance; 8. Testing the general linear hypothesis; Multivariate analysis of variance; 9. Testing independence of sets of variates; 10. Testing hypothesis of equality of coariance matrices and equality of mean vectors and covariance matrices; 11. Principal components; 12. Canonical correlations and canonical variables; 13. The distributions of characteristic roots and vectors; 14. Factor analysis.

Applied Multivariate Statistical Analysis (Classic Version)

Applied Multivariate Statistical Analysis (Classic Version)
Author: Richard A. Johnson
Publisher: Pearson
Total Pages: 808
Release: 2018-03-18
Genre: Multivariate analysis
ISBN: 9780134995397

Download Applied Multivariate Statistical Analysis (Classic Version) Book in PDF, Epub and Kindle

This title is part of the Pearson Modern Classics series. Pearson Modern Classics are acclaimed titles at a value price. Please visit www.pearsonhighered.com/math-classics-series for a complete list of titles. For courses in Multivariate Statistics, Marketing Research, Intermediate Business Statistics, Statistics in Education, and graduate-level courses in Experimental Design and Statistics. Appropriate for experimental scientists in a variety of disciplines, this market-leading text offers a readable introduction to the statistical analysis of multivariate observations. Its primary goal is to impart the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analyzing multivariate data, the text assumes two or more statistics courses as a prerequisite.

Multivariate Statistical Analysis

Multivariate Statistical Analysis
Author: Parimal Mukhopadhyay
Publisher: World Scientific
Total Pages: 568
Release: 2009
Genre: Mathematics
ISBN: 9812791752

Download Multivariate Statistical Analysis Book in PDF, Epub and Kindle

"This textbook presents a classical approach to some techniques of multivariate analysis in a simple and transparent manner. It offers clear and concise development of the concepts; interpretation of the output of the analysis; and criteria for selection of the methods, taking into account the strengths and weaknesses of each." "This book is ideal as an advanced textbook for graduate students in statistics and other disciplines like social, biological and physical sciences. It will also be of benefit to professional statisticians." --Book Jacket.