Synopses & Reviews
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. Representing a major improvement upon the standard "black box" approach to analyzing economic model structure, it introduces the powerful concept of parameter sensitivity analysis within a linear systems root/vector framework. The approach is illustrated with a good mediumsize econometric model (Michigan Quarterly Econometric Model of the United States). EISPACK, the Fortran code for computing characteristic roots and vectors has been upgraded and augmented by a model linearization code and a broader algorithmic framework. Also features an interface between the algorithmic code and the interactive modeling system (TROLL), making an unusually wide range of linear systems methods accessible to economists, operations researchers, engineers and physical scientists. 1985 (0-471-81930-1) 324 pp. Linear Statistical Models and Related Methods With Applications to Social Research John Fox A comprehensive, modern treatment of linear models and their variants and extensions, combining statistical theory with applied data analysis. Considers important methodological principles underlying statistical methods. Designed for researchers and students who wish to apply these models to their own work in a flexible manner. 1984 (0 471-09913-9) 496 pp. Statistical Methods for Forecasting Bovas Abraham and Johannes Ledolter This practical, user-oriented book treats the statistical methods and models used to produce short-term forecasts. Provides an intermediate level discussion of a variety of statistical forecasting methods and models and explains their interconnections, linking theory and practice. Includes numerous time-series, autocorrelations, and partial autocorrelation plots. 1983 (0 471-86764-0) 445 pp.
Synopsis
In recent years, an enormous amount of work has been done in the field of multivariate analysis, and a growing number of social, behavioral, and biological professionals have come to rely on these techniques for their research needs. Multivariate Analysis: Methods and Applications is an in-depth guide to multivariate methods. Employing a minimum of mathematical theory, it uses real data from a wide range of disciplines to illustrate not only ideas and applications, but also the subtleties of these methods. Special coverage of important topics not found in other general multivariate texts includes: multidimensional scaling, cross-classified categorical data, latent structure analysis, and linear structural relations (LISREL). A technical appendix reviews linear algebra and matrices and contains some distributional results dealing with the multivariate normal, multinomial, and Wishart distributions.
About the Author
About the authors William R. Dillon is Professor of Marketing at the University of Massachusetts. Dr. Dillon is the co-author of Discrete Discriminant Analysis and is on the editorial boards of the Journal of Business Research and Journal of Marketing Research. Dr. Dillon earned his PhD in marketing and quantitative methods at the City University of New York. Matthew Goldstein is President of the Research Foundation of the City University of New York and Professor of Statistics at Baruch College, City University of New York. He is a co-author of Discrete Discriminant Analysis and intermediate Statistical Methods. Dr Goldstein has served as president of the New York Area Chapter of the American Statistical Association. He earned his PhD in statistics at the University of Connecticut.
Table of Contents
Selected Aspects of Multivariate Analysis.
Principal Components Analysis.
Factor Analysis.
Multidimensional Scaling.
Cluster Analysis.
Multiple Regression.
Some Practical Considerations: Data Analysis Problems.
Cross-Classified Frequency Data.
Canonical Correlation Analysis.
Discriminant Analysis: The Two-Group Problem.
Multiple Discriminant Analysis and Related Topics.
Linear Structural Relations (LISREL).
Latent Structure Analysis.
Appendixes.
References.
Index.