A Robust Approach to Nonlinear Multivariate Analysis PDF Download
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Author: Peter Verboon
Publisher:
ISBN: 9789090070094
Category : Multivariate analysis
Languages : en
Pages : 189
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Book Description
Author: Peter Verboon
Publisher:
ISBN: 9789090070094
Category : Multivariate analysis
Languages : en
Pages : 189
Get Book Here
Book Description
Author: Peter Verboon
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 214
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Book Description
Author: Jacqueline Meulman
Publisher:
ISBN:
Category : Distance geometry
Languages : en
Pages : 222
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Book Description
Multivariate analysis and multidimensional scaling. Classification of MVA techniques: join and meet. STRIFE. STRAIN. STRESS. The majorization algorithm-model. Notation. Multivariate analysis via strife. The gifi system of nonlinear multivariate analysis. Join and meet. Homogeneity analysis. Principal components analysis. Classical principal components analysis. Nonlinear principal components analysis via join-STRIFE. Nonlinear principal components analysis via Meet-STRIFE. Canonical coordinates analysis. Classical canonical coordinates analysis. Nonlinear canonical coordinates analysis. Normalization. Multivariate analysis via strain. Distance properties of multivariate analysis. Join and meet. Principal components analysis. Canonical coordinates analysis. Homogeneity analysis. The asymmetric treatment of rows and columns. From classical to nonlinear multivariate analysis. Multivariate analysis via stress. From STRAIN to STRESS via multidimensional scaling. Multidimensional scaling with restrictions. Join and Meet. Homogeneity analysis. Principal components analysis. Classical principal components analysis. Nonlinear principal components analysis. Canonical coordinates analysis. Classical canonical coordinates analysis. Nonlinear canonical coordinates analysis. Cone regression with a non-diagonal weight matrix. Summary of the algorithm for nonlinear canonical coordinates analysis. Some special techniques in the stress framework. Simultaneous nonlinear principal components analysis with M sets. Asymmetric treatment of sets. Nonlinear redundancy analysis. Distance driven subspace approximation. Composite nonlinear principal components analysis. The majorization algorithm for multidimensional scaling. Overview of applications. Applications of multivariate analysis via strife. Homogeneity analysis of cetacea. Nonlinear principal components analysis of the United States. Nonlinear canonical coordinates analysis of the United States. Applications of multivariate analysis via strain. Principal coordinates analysis of city crime. Principal components analysis of city crime. Canonical coordinates analysis of the Southern States. Correspondence analysis and principal coordinates analysis of the parliament. Principal components analysis and classical MDS of criminal correlations. Applications of multivariate analysis via stress. Principal coordinates analysis of the parliament. Principal coordinates analysis of the parliament via constrained MDS. Homogeneity analysis of cetacea. Classical principal components analysis of city crime. Nonlinear principal components analysis of the United States. Classical canonical coordinates analysis of the Southern State. Nonlinear canonical coordinates analysis of the United States. Nonlinear redundancy analysis of the United States. Composite nonlinear principal components analysis of the United States.
Author:
Publisher:
ISBN: 9788126545117
Category : Nonlinear analysis
Languages : en
Pages : 579
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Book Description
Author: Jacqueline Jacinthe Meulman
Publisher:
ISBN:
Category :
Languages : en
Pages : 212
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Book Description
Author: Albert Gifi
Publisher: John Wiley & Sons
ISBN:
Category : Mathematics
Languages : en
Pages : 608
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Book Description
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.
Author: Hossein Riazoshams
Publisher: John Wiley & Sons
ISBN: 1119010454
Category : Mathematics
Languages : en
Pages : 264
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Book Description
The first book to discuss robust aspects of nonlinear regression—with applications using R software Robust Nonlinear Regression: with Applications using R covers a variety of theories and applications of nonlinear robust regression. It discusses both parts of the classic and robust aspects of nonlinear regression and focuses on outlier effects. It develops new methods in robust nonlinear regression and implements a set of objects and functions in S-language under SPLUS and R software. The software covers a wide range of robust nonlinear fitting and inferences, and is designed to provide facilities for computer users to define their own nonlinear models as an object, and fit models using classic and robust methods as well as detect outliers. The implemented objects and functions can be applied by practitioners as well as researchers. The book offers comprehensive coverage of the subject in 9 chapters: Theories of Nonlinear Regression and Inference; Introduction to R; Optimization; Theories of Robust Nonlinear Methods; Robust and Classical Nonlinear Regression with Autocorrelated and Heteroscedastic errors; Outlier Detection; R Packages in Nonlinear Regression; A New R Package in Robust Nonlinear Regression; and Object Sets. The first comprehensive coverage of this field covers a variety of both theoretical and applied topics surrounding robust nonlinear regression Addresses some commonly mishandled aspects of modeling R packages for both classical and robust nonlinear regression are presented in detail in the book and on an accompanying website Robust Nonlinear Regression: with Applications using R is an ideal text for statisticians, biostatisticians, and statistical consultants, as well as advanced level students of statistics.
Author: Sadanori Konishi
Publisher: CRC Press
ISBN: 1466567295
Category : Mathematics
Languages : en
Pages : 338
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Book Description
Select the Optimal Model for Interpreting Multivariate DataIntroduction to Multivariate Analysis: Linear and Nonlinear Modeling shows how multivariate analysis is widely used for extracting useful information and patterns from multivariate data and for understanding the structure of random phenomena. Along with the basic concepts of various procedu
Author: Shizuhiko Nishisato
Publisher: CRC Press
ISBN: 9781584886129
Category : Mathematics
Languages : en
Pages : 336
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Book Description
Quantification of categorical, or non-numerical, data is a problem that scientists face across a wide range of disciplines. Exploring data analysis in various areas of research, such as the social sciences and biology, Multidimensional Nonlinear Descriptive Analysis presents methods for analyzing categorical data that are not necessarily sampled randomly from a normal population and often involve nonlinear relations. This reference not only provides an overview of multidimensional nonlinear descriptive analysis (MUNDA) of discrete data, it also offers new results in a variety of fields. The first part of the book covers conceptual and technical preliminaries needed to understand the data analysis in subsequent chapters. The next two parts contain applications of MUNDA to diverse data types, with each chapter devoted to one type of categorical data, a brief historical comment, and basic skills peculiar to the data types. The final part examines several problems and then concludes with suggestions for future progress. Covering both the early and later years of MUNDA research in the social sciences, psychology, ecology, biology, and statistics, this book provides a framework for potential developments in even more areas of study.
Author: Shizuhiko Nishisato
Publisher: Springer Nature
ISBN: 9811624704
Category : Social Science
Languages : en
Pages : 231
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Book Description
This book offers a new look at well-established quantification theory for categorical data, referred to by such names as correspondence analysis, dual scaling, optimal scaling, and homogeneity analysis. These multiple identities are a consequence of its large number of properties that allow one to analyze and visualize the strength of variable association in an optimal solution. The book contains modern quantification theory for analyzing the association between two and more categorical variables in a variety of applicative frameworks. Visualization has attracted much attention over the past decades and given rise to controversial opinions. One may consider variations of plotting systems used in the construction of the classic correspondence plot, the biplot, the Carroll-Green-Schaffer scaling, or a new approach in doubled multidimensional space as presented in the book. There are even arguments for no visualization at all. The purpose of this book therefore is to shed new light on time-honored graphical procedures with critical reviews, new ideas, and future directions as alternatives. This stimulating volume is written with fresh new ideas from the traditional framework and the contemporary points of view. It thus offers readers a deep understanding of the ever-evolving nature of quantification theory and its practice. Part I starts with illustrating contingency table analysis with traditional joint graphical displays (symmetric, non-symmetric) and the CGS scaling and then explores logically correct graphs in doubled Euclidean space for both row and column variables. Part II covers a variety of mathematical approaches to the biplot strategy in graphing a data structure, providing a useful source for this modern approach to graphical display. Part II is also concerned with a number of alternative approaches to the joint graphical display such as bimodal cluster analysis and other statistical problems relevant to quantification theory.
