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

This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables.

- Covers a wide class of important models
- Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data
- Includes illustrative examples with real data sets from business, education, medicine, public health and sociology.
- Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.

Structural Sensitivity in Econometric Models Edwin Kuh, John W. Neese and Peter Hollinger Provides a pathbreaking assessment of the worth of linear dynamic systems methods for probing the behavior of complex macroeconomic models.

A practical guide for multivariate statistical techniques-- now updated and revised

In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data-based interpretations relevant to multivariate analysis in a way that addresses real-world problems arising in many...

This volume makes up one of two books in a series on continuous multivariate distributions (CMD). Johnson and Kotz published the original, single volume in 1972. The subject has developed greatly since that time, with possible applications such as understanding the complexities of flood, quake, rainfall, and wind control, and now constitutes two broadly classified areas: sampling and modelling.

This text is about ideas for the graphical analysis of regression data. A statistical context for regression and regression graphics is emphasized. This new context is intended to blend with, rather than replace, most traditional paradigms for regression analysis. Residuals are an important part of graphics for regression analysis, and they play key roles in this book. But they are not singled out for special study; rather, they occur throughout the volume in different roles depending on the context.

An up-to-date approach to understanding statistical inference

Statistical inference is finding useful applications in numerous fields, from sociology and econometrics to biostatistics. This volume enables professionals in these and related fields to master the concepts of statistical inference under inequality constraints and to apply the theory to problems in a variety of areas.

Constrained Statistical Inference: Order, Inequality, and Shape Constraints provides a unified and up-to-date treatment of the methodology. It clearly illustrates concepts with practical examples from a variety...

A precise and accessible presentation of linear model theory, illustrated with data examples

Statisticians often use linear models for data analysis and for developing new statistical methods. Most books on the subject have historically discussed univariate, multivariate, and mixed linear models separately, whereas Linear Model Theory: Univariate, Multivariate, and Mixed Models presents a unified treatment in order to make clear the distinctions among the three classes of models.

Linear Model Theory: Univariate, Multivariate, and Mixed Models begins with six...

Features and capabilities of the REG, ANOVA, and GLM procedures are included in this introduction to analysing linear models with the SAS System. This guide shows how to apply the appropriate procedure to data analysis problems and understand PROC GLM output. Other helpful guidelines and discussions cover the following significant areas: Multivariate linear models; lack-of-fit analysis; covariance and heterogeneity of slopes; a classification with both crossed and nested effects; and analysis of variance for balanced data. This fourth edition includes updated examples, new software-related...

Accessible, up-to-date coverage of a broad range of modern and traditional methods. The ability to understand and analyze categorical, or count, data is crucial to the success of statisticians in a wide variety of fields, including biomedicine, ecology, the social sciences, marketing, and many more. "Statistical Analysis of Categorical Data" provides thorough, clear, up-to-date explanations of all important methods of categorical data analysis at a level accessible to anyone with a solid undergraduate knowledge of statistics.

Featuring a liberal use of real-world examples as well as a...

Perfected over three editions and more than forty years, this field- and classroom-tested reference:
* Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures.
* Treats all the basic and important topics in multivariate statistics.
* Adds two new chapters, along with a number of new sections.
* Provides the most methodical, up-to-date information on MV statistics available.

An effective technique for data analysis in the social sciences

The recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts and slopes that permit each case in a sample to have a different trajectory over time. Furthermore, researchers can include variables to predict the parameters governing these trajectories.

The authors synthesize a vast amount of research and...

Simplifies the treatment of statistical inference focusing on how to specify and interpret models in the context of testing causal theories. Simple bivariate regression, multiple regression, multiple classification analysis, path analysis, logit regression, multinomial logit regression and survival models are among the subjects covered. Features an appendix of computer programs (for major statistical packages) that are used to generate illustrative examples contained in the chapters.

Statistical Factor Analysis and Related Methods Theory and Applications In bridging the gap between the mathematical and statistical theory of factor analysis, this new work represents the first unified treatment of the theory and practice of factor analysis and latent variable models. It focuses on such areas as:
* The classical principal components model and sample-population inference
* Several extensions and modifications of principal components, including Q and three-mode analysis and principal components in the complex domain
* Maximum likelihood and weighted factor...

Provides state-of-the-art coverage for the researcher confronted with designing and executing a simulation study using continuous multivariate distributions. Concise writing style makes the book accessible to a wide audience. Well-known multivariate distributions are described, emphasizing a few representative cases from each distribution. Coverage includes Pearson Types II and VII elliptically contoured distributions, Khintchine distributions, and the unifying class for the Burr, Pareto, and logistic distributions. Extensively illustrated--the figures are unique, attractive, and reveal very...

Graphical models--a subset of log-linear models--reveal the interrelationships between multiple variables and features of the underlying conditional independence. Following the theorem-proof-remarks format, this introduction to the use of graphical models in the description and modeling of multivariate systems covers conditional independence, several types of independence graphs, Gaussian models, issues in model selection, regression and decomposition. Many numerical examples and exercises with solutions are included.

This is a programmatic statement of Gifi's general philosophy and the way in which it is implemented on the digital computer. It is a non-standard treatment of multivariate data analysis, with the emphasis on plotting observations and variables in low dimensional Euclidean space. Less emphasis than usual is given to regression and analysis of variance.

Geared toward upper-level undergraduates and graduate students, this two-part treatment deals with the foundations of multivariate analysis as well as related models and applications.
Starting with a look at practical elements of matrix theory, the text proceeds to discussions of continuous multivariate distributions, the normal distribution, and Bayesian inference; multivariate large sample distributions and approximations; the Wishart and other continuous multivariate distributions; and basic multivariate statistics in the normal distribution. The second half of the text moves from...

In Decision Space: Multidimensional Utility Analysis, Paul Weirich increases the power and versatility of utility analysis and in the process advances decision theory. Combining traditional and novel methods of option evaluation into one systematic method, multidimensional utility analysis is a valuable new tool. The multiple dimensions of this analysis create a decision space broad enough to accommodate all factors affecting an option's utility. The book will be of interest to advanced students and professionals working in the subject of decision theory, as well as to economists and other...

Almost all published results on multivariate t-distributions (spanning the last 50 years) are collected in this comprehensive reference. The book begins with theoretical probabilistic results and then presents statistical aspects. Generalizations and applications are dealt with in the final chapters, including material on estimation and regression models of special value for practitioners in statistics and economics. A comprehensive bibliography of over 350 references is included.

This volume presents a practical and unified approach to categorical data analysis based on the Akaike Information Criterion (AIC) and the Akaike Bayesian Information Criterion (ABIC). Conventional procedures for categorical data analysis are often inappropriate because the classical test procedures employed are too closely related to specific models. The approach described in this volume enables actual problems encountered by data analysts to be handled much more successfully. Amongst various topics explicitly dealt with are the problem of variable selection for categorical data, a Bayesian...