ISBN: 978-0-470-64183-5
Hardcover
232 pages
June 2011
A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Introduction to Statistical Learning Theory provides a broad and accessible introduction to rapidly evolving field of statistical pattern recognition and statistical learning theory. Exploring topics that are not often covered in introductory level books on statistical learning theory, including PAC learning, VC dimension, and simplicity, the authors present upper-undergraduate and graduate levels with the basic theory behind contemporary machine learning and uniquely suggest it serves as an excellent framework for philosophical thinking about inductive inference.
ISBN: 978-0-470-54064-0
Hardcover
356 pages
July 2011
Times Series Analysis and Forecasting presents seemingly difficult techniques and methodologies in an insightful and application-based way. Through a hands-on and user-friendly approach, this text includes exercises, graphical techniques, examples, excel spreadsheets, and software applications on time series analysis. The reference offers step-by-step procedures and instructions. This textbook is essential for students, emphasizing intuitive learning rather than theory through modeling the data in careful interpretation and use of modern statistical graphics
ISBN: 978-0-470-60445-8
Hardcover
576 pages
July 2011
Understanding approximate dynamic programming (ADP) in large industrial settings helps develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. With a focus on modeling and algorithms in conjunction with the language of mainstream operations research, artificial intelligence, and control theory, this second edition of Approximate Dynamic Programming Solving the Curses of Dimensionality uniquely integrates four distinct disciplines Markov design processes, mathematical programming, simulation, and statistics to show students, practitioners, and researchers how to successfully model and solve a wide range of real-life problems using ADP.
ISBN: 978-0-471-74896-0
Hardcover
480 pages
August 2011
Excellent discussions of assumptions, comprehensive interpretations of results, and numerous comparisons of various multiple comparison procedures are the hallmarks of this book on analysis of covariance. Detailed calculations are presented throughout with sensitivity to problems of multiple inference. The author extensively discusses the problems caused by violation of the mathematical assumptions and he extends an invaluable amount of detail on examples of interpretations. The synthesis of the literature is a contribution in and of itself. Written for the applied researcher, the book also finds a niche among experimental design and statistical regression courses in a variety of fields from engineering to the social sciences as supplemental reading.
Key features include:
*Detailed descriptions of assumptions, the consequences of violating assumptions, and alternative procedures to follow are explained throughout the book.
*Interpretation issues associated with each experimental design are clearly and straightforwardly discussed.
*Easily understood descriptions of complex analyses such as multiple covariate analysis, multivariate ANCOVA, rank ANCOVA, the Johnson-Neyman *rocedure, and nonlinear ANCOVA, among many others, are exploited to the fullest.
*There is a complete selection of Bonferroni F tables and other previously unpublished tables included at the rear of the book.
*Chapter recommendations, summaries, and software discussions have been added to the new edition.
ISBN: 978-0-470-59074-4
Hardcover
648 pages
August 2011
Statistical methods for quality improvement offer numerous benefits for industry and business, both through identifying existing trouble spots and alerting management and technical personnel to potential problems. An easy-to-read and easy-to-follow guide based on intuitive reasoning rather than heavy mathematica, this fully expanded and revised third edition of Statistical Methods for Quality Improvement offers upper-level undergraduate and graduate students clear, thorough coverage of all available techniques from basic control charts to regression and design of experiments as well as the combined use of these tools.
ISBN: 978-0-470-63795-1
Hardcover
608 pages
August 2011
Principles of Linear Algebra with MathematicaR uniquely addresses the quickly growing intersection between subject theory and numerical computation. Computer algebra systems such as MathematicaR are becoming ever more powerful, useful, user friendly and readily available to the average student and professional, but thre are few books which currently cross this gap between linear algebra and MathematicaR. This book introduces algebra topics which can only be taught with the help of computer algebra systems, and the authors include all of the commands required to solve complex and computationally challenging linear algebra problems using MathematicaR. The book begins with an introduction to the commands and programming guidelines for working with MathematicaR. Next, the authors explore linear systems of equations and matrices, applications of linear systems and matrices, determinants, inverses, and Cramer’s rule. Basic linear algebra topics, such as vectors, dot product, cross product, vector projection, are explored as well as the more advanced topics of rotations in space, rolling a circle along a curve, and the TNB Frame. Subsequent chapters feature coverage of linear programming, linear transformations from Rn to Rm, the geometry of linear and affine transformations, and least squares fits and pseudoinverses. Although computational in nature, the material is not presented in a simply theory-proof-problem format. Instead, all topics are explored in a reader-friendly and insightful way. The MathematicaR software is fully utilized to highlight the visual nature of the topic, as the book is complete with numerous graphics in two and three dimensions, animations, symbolic manipulations, numerical computations, and programming. Exercises are supplied in most chapters, and a related Web site houses MathematicaR code so readers can work through the provided examples.
ISBN: 978-0-470-97192-5
Hardcover
280 pages
September 2011
This book provides a comprehensive and unified approach to factor analysis and latent variable modeling and theory, providing a unified and coherent treatment from a statistical perspective. A general framework is presented to enable the derivation of the commonly used models. Updated numerical examples are provided as well as the software to carry them out.
Written by leading experts in the field, Latent Variable Models and Factor Analysis:
*Includes new topics such as, covariate effects and non-linear terms, multiple population analysis and univariate and bivariate margins.
*Provides a new section on structural equation models (SEM) and Markov Chain Monte Carlo methods, along with illustrative examples.
*Looks at estimation methods, goodness-of-fit, non-linear models, covariates, longitudinal data and multilevel modeling along with updated examples throughout.
*Unifies many different streams of latent variable modeling and probability modeling.
An introductory section is provided, which looks at the nature and interpretation of a latent variable, motivating discussions of closely related methods which make little or no explicit use of latent variables. Principal components are discussed in more depth, exploring its relationship to factor analysis in both historical and contemporary and theoretically and empirically. Furthermore, the book explores The Bonds’ Model for abilities, a model which has a correlation structure which is identical to that of the factor model and hence cannot be distinguished from it and does not involve latent variables.