Principles Of Multivariate Analysis

Author: Wojtek Krzanowski
Publisher: OUP Oxford
ISBN: 0191053945
Size: 16.80 MB
Format: PDF, ePub, Mobi
View: 4704
Download Read Online

This book is an introduction to the principles and methodology of modern multivariate statistical analysis. It is written for the user and potential user of multivariate techniques as well as for students coming to the subject for the first time. The authors emphasis is problem-orientated and he is at pains to stress geometrical intuition in preference to algebraic manipulation. Mathematical sections that are not essential for a practical understanding of thetechniques are clearly indicated so that they may be skipped by the non-specialist. Discrete and mixed variable techniques are presented as well as continuous variable techniques to give a comprehensive coverage of the subject. This updated edition includes a new appendix which traces developmentsthat have taken place in the years since the publication of the first edition and which clarifies some issues raised by readers of the original text. References to about 60 recent books and articles supplement the material in this appendix. Overall, this volume provides an up-to-date and readable practical account of the subject, both for students of statistics and for research workers in subjects as diverse as anthropology, education, industry, medicine and taxonomy. The new editionincludes a survey of the most recent developments in the subject.

Multivariate Analysis

Author: W. J. Krzanowski
Publisher: Oxford University Press
ISBN: 9780340593257
Size: 34.92 MB
Format: PDF, Mobi
View: 6294
Download Read Online

The second of a two-part work on multivariate analysis, this volume covers more specialized techniques; topics include discriminant analysis, cluster analysis, path analysis, graphical modelling, latent variable techniques, repeated measures analysis and growth curve models. Modern problems and techniques, such as handling of high dimensional data and the use of neural networks are featured and the book concludes with a discussion of strategic aspects of multivariate analysis.

Nonlinear Multivariate Analysis

Author: Albert Gifi
Publisher: John Wiley & Sons Incorporated
ISBN: 9780471926207
Size: 70.79 MB
Format: PDF, Mobi
View: 2297
Download Read Online

Conventions and controversies in multivariate analysis; Coding of categorical data; Homogeneity analysis; Nonlinear principal components analysis; Nonlinear generalized canonical analysis; Nonlinear canonical correlation analysis; Asymmetric treatment of sets: some special cases, some future programs; Multidimensional scaling and correspondende analysis; Models as gauges for the analysis of binary data; Reflections on restrictions; Nonlinear multivariate analysis: principles and possibilities; The study of stability; The proof of the pudding.

Applied Multivariate Analysis

Author: Neil H. Timm
Publisher: Springer Science & Business Media
ISBN: 0387227717
Size: 68.73 MB
Format: PDF, ePub, Docs
View: 4072
Download Read Online

This book provides a broad overview of the basic theory and methods of applied multivariate analysis. The presentation integrates both theory and practice including both the analysis of formal linear multivariate models and exploratory data analysis techniques. Each chapter contains the development of basic theoretical results with numerous applications illustrated using examples from the social and behavioral sciences, and other disciplines. All examples are analyzed using SAS for Windows Version 8.0.

Applied Multivariate Statistical Analysis

Author: Wolfgang Karl Härdle
Publisher: Springer
ISBN: 3662451719
Size: 14.19 MB
Format: PDF, Kindle
View: 2526
Download Read Online

Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners. It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added. All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior. All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate Statistical Analysis offers the following new features: A new chapter on Variable Selection (Lasso, SCAD and Elastic Net) All exercises are supplemented by R and MATLAB code that can be found on www.quantlet.de. The practical exercises include solutions that can be found in Härdle, W. and Hlavka, Z., Multivariate Statistics: Exercises and Solutions. Springer Verlag, Heidelberg.

The Chicago Guide To Writing About Multivariate Analysis

Author: Jane E. Miller
Publisher: University of Chicago Press
ISBN: 0226527840
Size: 54.46 MB
Format: PDF, Mobi
View: 3813
Download Read Online

Writing about multivariate analysis is a surprisingly common task. Researchers use these advanced statistical techniques to examine relationships among multiple variables, such as exercise, diet, and heart disease, or to forecast information such as future interest rates or unemployment. Many different people, from social scientists to government agencies to business professionals, depend on the results of multivariate models to inform their decisions. At the same time, many researchers have trouble communicating the purpose and findings of these models. Too often, explanations become bogged down in statistical jargon and technical details, and audiences are left struggling to make sense of both the numbers and their interpretation. Here, Jane Miller offers much-needed help to academic researchers as well as to analysts who write for general audiences. The Chicago Guide to Writing about Multivariate Analysis brings together advanced statistical methods with good expository writing. Starting with twelve core principles for writing about numbers, Miller goes on to discuss how to use tables, charts, examples, and analogies to write a clear, compelling argument using multivariate results as evidence. Writers will repeatedly look to this book for guidance on how to express their ideas in scientific papers, grant proposals, speeches, issue briefs, chartbooks, posters, and other documents. Communicating with multivariate models need never appear so complicated again.

Multivariate Statistical Methods In Behavioral Research

Author: Richard Darrell Bock
Publisher: McGraw-Hill Companies
ISBN:
Size: 22.29 MB
Format: PDF, ePub, Docs
View: 2568
Download Read Online

The role of multivariate statistical methods in behavioral research; Mathematical prerequisites for multivariate analysis; The multivariate normal distribution; Principles and methods of multivariate least-squares estimation; Linear models for designed experiments; Linear models in nonexperimental studies; Analysis of repeated measurements; Multivariate analysis of qualitative data.

Principles Of Statistics

Author: M. G. Bulmer
Publisher: Courier Corporation
ISBN: 9780486637600
Size: 25.45 MB
Format: PDF, ePub
View: 827
Download Read Online

Concise description of classical statistics, from basic dice probabilities to modern regression analysis. Equal stress on theory and applications. Moderate difficulty; only basic calculus required. Includes problems with answers.

Techniques And Applications Of Hyperspectral Image Analysis

Author: Hans Grahn
Publisher: John Wiley & Sons
ISBN: 9780470010877
Size: 52.89 MB
Format: PDF, Kindle
View: 2066
Download Read Online

Techniques and Applications of Hyperspectral Image Analysis gives an introduction to the field of image analysis using hyperspectral techniques, and includes definitions and instrument descriptions. Other imaging topics that are covered are segmentation, regression and classification. The book discusses how high quality images of large data files can be structured and archived. Imaging techniques also demand accurate calibration, and are covered in sections about multivariate calibration techniques. The book explains the most important instruments for hyperspectral imaging in more technical detail. A number of applications from medical and chemical imaging are presented and there is an emphasis on data analysis including modeling, data visualization, model testing and statistical interpretation.