ISBN: 978-0-471-74869-4
Hardcover
559 pages
November 2007
This book emphasizes computational statistics and algorithms and includes numerous references to both the theory behind the methods and the applications of the methods. Each chapter consists of four parts: a definition followed by a list of results, a short list of references to related topics in the book (since some overlap is unavoidable), one or more references to proofs, and references to applications. Topics include special matrices, non-negative matrices, special products and operators, Jacobians, partitioned and patterned matrices, matrix approximation, matrix optimization, multiple integrals and multivariate distributions, linear and quadratic forms, etc.
Table of contents
Preface.
1. Notation.
2. Vectors, Vector Spaces, and Convexity.
3. Rank.
4. Matrix Functions: Inverse, Transpose, Trace, Determinant, and Norm.
5. Complex, Hermitian, and Related Matrices.
6. Eigenvalues, Eigenvectors, and Singular Values.
7. Generalized Inverses.
8. Some Special Matrices.
9. Non-Negative Vectors and Matrices.
10. Positive Definite and Non-negative Definite Matrices.
11. Special Products and Operators.
12. Inequalities.
13. Linear Equations.
14. Partitioned Matrices.
15. Patterned Matrices.
16. Factorization of Matrices.
17. Differentiation and Finite Differences.
18. Jacobians.
19. Matrix Limits, Sequences and Series.
20. Random Vectors.
21. Random Matrices.
22. Inequalities for Probabilities and Random Variables.
23. Majorization.
24. Optimization and Matrix Approximation.
References.
Index.
ISBN: 978-0-470-17132-5
Hardcover
440 pages
January 2008
This is the first book to utilize computer software (Maple? and Mathematica) to do the types of linear programming involved in game theory, allowing students and readers to solve many more advanced and interesting games without spending time on the theory of linear programming. The focus of the book is not on proofs, but some proofs are provided for important results. Algorithms for solutions to the games are presented in detail, and interesting applications are used to illustrate the theory.
Table of contents
Preface.
Acknowledgments.
Introduction.
1. Matrix 2 person games.
2. Solution Methods for Matrix Games.
3. Two Person Nonzero Sum Games.
4. N Person Nonzero Sum Games with a Continuum of Strategies.
5. Cooperative games.
6. Evolutionary Stable Strategies and Population games.
Problem Solutions.
References.
Index.
ISBN: 978-0-471-75499-2
Hardcover
420 pages
January 2008
This book fills this gap, providing a comprehensive, self-contained introduction to regression modeling used in the analysis of time-to-event data in epidemiological, biostatistical, and other health-related research. Unlike other texts on the subject, it focuses almost exclusively on practical applications rather than mathematical theory and it offers clear, accessible presentations of modern modeling techniques supplemented with real-world examples and case studies.
Table of contents
Preface.
1. Introduction to Regression Modeling of Survival Data.
2. Descriptive Methods for Survival Data.
3. Regression Models for Survival Data.
4. Interpretation of a Fitted Proportional Hazards Regression Model.
5. Model Development.
6. Assessment of Model Adequacy.
7. Extensions of the Proportional Hazards Model.
8. Parametric Regression Models.
9. Other Models and Topics.
References.
Index.
ISBN: 978-0-470-01491-2
Hardcover
544 pages
March 2008
An accessible account of how to prepare and interpret statistical models of point patterns using spatial data from biology, ecology, and forestry.
This new book from the Statistics in Practice series, earth and environmental statistics section, provides an accessible introduction to spatial point patterns, taking particular account of spatial data. Written by authors experienced in both theoretical and applied aspects of the subject, this book makes an ideal introduction or reference text. Application oriented and data-driven, this book includes examples from biology, geology, ecology and environmental sciences as well as forestry.
Combines well-presented theory and ehow-tof examples.
Includes the analysis of complex data sets.
With examples of datasets on an accompanying web-site.
Ideal for applied researchers in environmental statistics, ecology and geostatistics this book would also benefit students of statistics and graduate students of applied science who need a reference text on the topic.
Table of contents
Preface.
List of Examples.
1. Introduction.
2. The Homogeneous Poisson point process.
3. Finite point processes.
4. Stationary point processes.
5. Stationary marked point processes.
6. Modelling and simulation of stationary point processes.
7. Fitting and testing point process models.
References.
Notation index.
Author index.
Subject index.
ISBN: 978-0-471-65397-4
Hardcover
464 pages
February 2008
Introduction to Time Series Analysis and Forecasting examines methods for modeling and analyzing time series data with a view towards drawing inferences about the data and generating forecasts that will be useful to the decision maker. While the level is advanced undergraduate/first-year graduate, with a prerequisite knowledge of basic statistical methods, some portions of the book require a first course in calculus and modest matrix algebra manipulation skills. Minitab and SAS Software System are used extensively to illustrate how the methods in the text are implemented in practice.
Table of contents
1. Introduction to Forecasting.
2. Statistics Background for Forecasting.
3. Regression Analysis and Forecasting.
4. Exponential Smoothing Methods.
5. Autoregressive Integrated Moving Average (ARIMA) Models.
6. Transfer Function and Intervention Models.
7. Survey of Other Forecasting Methods.
Bibliography.
Index.