Kategoria BISAC: Mathematics >> Probability & Statistics - Multivariate Analysis

Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods.

There are many books covering survival analysis, but very few that cover the multivariate...

This monograph is multivariate, multi-perspective and multipurpose. We intend to be innovatively integrative through statistical synthesis. Innovation requires capacity to operate in ways that are not ordinary, which means that conventional computations and generic graphics will not meet the needs of an adaptive approach. Flexible formulation and special schematics are essential elements that must be manageable and economical.

This book provides the reader with a background on simulating copulas and multivariate distributions in general. It unifies the scattered literature on the simulation of various families of copulas (elliptical, Archimedean, Marshall-Olkin type, etc.) as well as on different construction principles (factor models, pair-copula construction, etc.). The book is self-contained and unified in presentation and can be used as a textbook for advanced undergraduate or graduate students with a firm background in stochastics. Alongside the theoretical foundation, ready-to-implement algorithms and many...

This title offers extended versions of papers presented at the Third International Conference on Mathematical Methods in Reliability, held in 2002. It includes a historical review of the field, and chapters on survival analysis, software reliability, repairable systems reliability and more.

This book focuses on the algebraic-topological aspects of probability theory, leading to a wider and deeper understanding of basic theorems, such as those on the structure of continuous convolution semigroups and the corresponding processes with independent increments. The method applied within the setting of Banach spaces and of locally compact Abelian groups is that of the Fourier transform. This analytic tool along with the relevant parts of harmonic analysis makes it possible to study certain properties of stochastic processes in dependence of the algebraic-topological structure of their...

Clarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods

Featuring a thoroughly revised presentation, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the Second Edition presents additional clarification of theoretical opportunities, new algorithms, and up-to-date coverage of the unique...

Non-linear analysis of categorical variables, that is, a variable that can sort objects into a limited number of distinct groups called 'categories', is a useful technique for social scientists, particularly those who do survey research. This book introduces the reader to the application of a particular approach to categorical analysis, the GIFI system, or multiple correspondence analysis. Using illustrative examples from a variety of disciplines, van de Geer shows how to perform these techniques using standard computer programs, such as SPSS. The book explains when to use particular...

Praise for the Second Edition

"This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere."
--IIE Transactions

Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and...

A practical introduction to using Mplus for the analysis of multivariate data, this volume provides step-by-step guidance, complete with real data examples, numerous screen shots, and output excerpts.

An English translation of my "Statistisahe Tafeln zur multivariaten Analysis - Ein Handbuah mit Hinweisen zur Anwendung" was planned already in 1975 when I prepared the German volume. The tables were immediately supplied with German and English headings for the inten ded photo-offset printing. In the meantime. new and important tables for multivariate statistiaal hypotheses and proaedures have been aompiled and published. Only four of them have been inaorporated in the present volume. The seleation of these tables must be on an individual basis for reasons of spaae. Let me mention only the...

The aim of this book is to give an up to date account of the most commonly uses statisti cal models for categorical data. The emphasis is on the connection between theory and applications to real data sets. The book only covers models for categorical data. Various models for mixed continuous and categorical data are thus excluded. The book is written as a textbook, although many methods and results are quite recent. This should imply, that the book can be used for a graduate course in categorical data analysis. With this aim in mind chapters 3 to 12 are concluded with a set of exer cises. In...

In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data.

• How can regression analysis and PCA be combined in a beneficial way?
• Why and when is it a good idea to combine them?
• What kind of benefits are we getting from them?

Addressing these questions, Constrained Principal Component Analysis and Related Techniques shows how constrained PCA (CPCA) offers a unified...

Written by a prominent statistician and author, the first edition of this bestseller broke new ground in the then emerging subject of spatial statistics with its coverage of spatial point patterns. Retaining all the material from the second edition and adding substantial new material, Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition presents models and statistical methods for analyzing spatially referenced point process data.

Reflected in the title, this third edition now covers spatio-temporal point patterns. It explores the...

This book presents the latest theory and application of latent variable models (LVMs). With contributions from the most prominent researchers in the field, it addresses the use of the latest techniques such as factor analysis, structural equation modeling, and latent growth curve, multilevel, and item response theory models as well as different types of data structures and applications. Part 1 focuses on complexities which extend standard LVMs and Part 2 focuses on drawing real-world meaning from results obtained in LVM. Intended for researchers and students interested in the use of LVMs,...

The complexity of social problems necessitates that social work researchers understand and apply multivariate statistical methods in their investigations. In this pocket guide, the authors introduce readers to three of the more frequently used multivariate methods in social work research with an emphasis on basic statistics. The primary aim is to prepare entry-level doctoral students and early career social work researchers in the use of multivariate methods by providing an easy-to-understand presentation, building on the basic statistics that inform them.

The pocket guide begins with a...

Featuring contributions from some of the leading researchers in the field of SEM, most chapters are written by the author(s) who originally proposed the technique and/or contributed substantially to its development. Content highlights include latent variable mixture modeling, multilevel modeling, interaction modeling, models for dealing with nonstandard and noncompliance samples, the latest on the analysis of growth curve and longitudinal data, specification searches, item parceling, and equivalent models. This volume will appeal to educators, psychologists, biologists, business...

The analysis of variance (ANOYA) models have become one of the most widely used tools of modern statistics for analyzing multifactor data. The ANOYA models provide versatile statistical tools for studying the relationship between a dependent variable and one or more independent variables. The ANOYA mod els are employed to determine whether different variables interact and which factors or factor combinations are most important. They are appealing because they provide a conceptually simple technique for investigating statistical rela tionships among different independent variables known as...

This bestseller is known for its modern, practical approach to regression-analysis methods that students will find applicable to real-life problems. APPLIED REGRESSION ANALYSIS AND MULTIVARIABLE METHODS highlights the role of the computer in contemporary statistics with numerous printouts and exercises that can be solved with the computer. The authors continue to emphasize model development, the intuitive logic and assumptions that underlie the techniques covered, the purposes, advantages, and disadvantages of the techniques, and valid interpretations of those techniques.

Multivariable analysis is of interest to pure and applied mathematicians, physicists, electrical, mechanical and systems engineers, mathematical economists, biologists, and statisticians. This book takes the student and researcher on a journey through the core topics of the subject. Systematic exposition, with numerous examples and exercises from the computational to the theoretical, makes difficult ideas as concrete as possible. Good bibliography and index.