ISBN: 978-0-470-27284-8
Hardcover
768 pages
June 2008
This is a revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application ?forecasting, model specification, estimation, modeling the effects of intervention events, and process control, among others. In addition to meticulous modifications in content and improvements in style, the new edition incorporates several new topics in an effort to modernize the subject matter. These topics include extensive discussions of multivariate time series, smoothing, likelihood function based on the state space model, autoregressive models, structural component models and deterministic seasonal components, and nonlinear and long memory models.
Preface.
1. Introduction.
PART I. STOCHASTIC MODELS AND THEIR FORECASTING.
2. Autocorrelation Function and Spectrum Of Stationary Processes.
3. Linear Stationary Models 47.
4. Linear Nonstationary Models.
5. Forecasting.
PART II. STOCHASTIC MODEL BUILDING.
6. Model Identification.
7. Model Estimation.
8. Model Diagnostic Checking.
9. Seasonal Models.
10. Nonlinear and Long Memory Models.
Part III. Transfer Function and Multivariate Model Building.
11. Transfer Function Models.
12. Identification, Fitting, and Checking Of Transfer Function Models.
13. Intervention Analysis Models and Outlier Detection.
14. Multivariate Time Series Analysis.
14.2.4 Relation of Vector ARMA to Transfer Function and.
PART IV. DESIGN OF DISCRETE CONTROL SCHEMES.
15. Aspects of Process Control.
PART V. CHARTS AND TABLES.
Collection of Tables and Charts.
Collection of Time Series Used For Examples in The Text and In Exercises.
References.
PART VI.
Exercises and Problems.
Index.
Series: Lecture Notes in Mathematics , Vol. 1947
Subseries: Fondazione C.I.M.E., Firenze
2008, Approx. 215 p., Softcover
ISBN: 978-3-540-79813-2
Due: July 2, 2008
Starting in the middle of the 80s, there has been a growing and fruitful interaction between algebraic geometry and certain areas of theoretical high-energy physics, especially the various versions of string theory. Physical heuristics have provided inspiration for new mathematical definitions (such as that of Gromov-Witten invariants) leading in turn to the solution of problems in enumerative geometry. Conversely, the availability of mathematically rigorous definitions and theorems has benefited the physics research by providing the required evidence in fields where experimental testing seems problematic. The aim of this volume, a result of the CIME Summer School held in Cetraro, Italy, in 2005, is to cover part of the most recent and interesting findings in this subject.
Preface by Kai Behrend and Marco Manetti.- Dan Abramovich: Lectures on Gromov?Witten Invariants of Orbifolds.- Marcos Marino: Lectures on the Topological Vertex.- Michael Thaddeus: Floer Cohomology with Gerbes.- Ravi Vakil: The Moduli Space of Curves and Gromov-Witten Theory.
Series: Use R
2008, Approx. 330 p., Softcover
ISBN: 978-0-387-77789-4
Due: August 2008
First guide for doing and developing modern morphometrics with R
Quantifying shape and size variation is essential in evolutionary biology and in many other disciplines. Since the "morphometric revolution of the 90s," an increasing number of publications in applied and theoretical morphometrics emerged in the new discipline of statistical shape analysis.
The R language and environment offers a single platform to perform a multitude of analyses from the acquisition of data to the production of static and interactive graphs. This offers an ideal environment to analyze shape variation and shape change. This open-source language is accessible for novices and for experienced users. Adopting R gives the user and developer several advantages for performing morphometrics: evolvability, adaptability, interactivity, a single and comprehensive platform, possibility of interfacing with other languages and software, custom analyses, and graphs. The book explains how to use R for morphometrics and provides a series of examples of codes and displays covering approaches ranging from traditional morphometrics to modern statistical shape analysis such as the analysis of landmark data, Thin Plate Splines, and Fourier analysis of outlines.
Contributors. - Abbreviations and notations. - Introduction. - Acquiring and manipulating morphometric data. - Traditional statistics for morphometrics. - Modern morphometrics based on configurations of landmarks. - Statistical analysis of shape using modern morphometrics. - Going further with R. - Appendix A: functions developed in this text. - Appendix B: packages used in this text. - References. - Index.
Series: Statistics for Biology and Health
2008, Approx. 320 p., Hardcover
ISBN: 978-0-387-78190-7
Due: August 2008
One of the most intriguing questions facing modern science is the inner workings of the human brain. Functional magnetic resonance imaging (fMRI) is a powerful tool used to study the human brain in action. The data produced from mapping the active processes within the brain present many challenges to statisticians, computer scientists, engineers and other data analysts, due to their complex structure and the ever-increasing sophistication of the scientific questions being posed by researchers. This book represents the first in-depth discussion of statistical methodology, which it couples with an introduction to the scientific background needed to understand the data.
Starting from the basic science - where fMRI data come from, why they are so complicated, and the role statistics can play in designing and interpreting experiments - the book gives a detailed survey of the numerous methods that have been applied in the last fifteen years. The analysis of fMRI data features many of the major issues of concern in modern statistics, such as high dimensionality, multiple testing, and visualization. The array of techniques examined in the book ranges from the simple two-sample t-test and the general linear model to hierarchical spatiotemporal models, multivariate methods such as principal components analysis, and Bayesian approaches as they have been used in fMRI. Software, including descriptions of the most popular freeware packages and their capabilities, is also discussed. This book offers researchers who are interested in the analysis of fMRI data a detailed discussion from a statistical perspective that covers the entire process from data collection to the graphical presentation of results. The book is a valuable resource for statisticians who want to learn more about this growing field, and for neuroscientists who want to learn more about how their data can be analyzed.
Nicole A. Lazar is Professor of Statistics at the University of Georgia and affiliated faculty of the Center for Health Statistics, University of Illinois at Chicago. She is a prominent researcher in this area, a contributor to the FIASCO software for fMRI data analysis, and heads an fMRI statistics research group at the University of Georgia.
The science of fMRI. - Design of fMRI experiments. - Noise and data preprocessing. - Statistical issuesin fMRI data analysis. - Basic statistical analysis. - Temporal, spatial, and spatiotemporal models. - Multivariate approaches. - Basis function approaches. - Bayesian methods in fMRI. - Multiple testing in fMRI: the problem of "thresholding."- Additional statistical issues. - Case study: eye motion data. - Survey of major fMRI software packages. - Glossary of fMRI terms. - References. - Index.
Series: Statistics and Computing
2008, Approx. 550 p., Hardcover
ISBN: 978-0-387-77371-1
Due: August 2008
This book is an integrated treatment of applied statistical methods, presented at an intermediate level, and the SAS programming language. It serves as an advanced introduction to SAS as well as how to use SAS for the analysis of data arising from many different experimental and observational studies. While there are many introductory texts on SAS programming, statistical methods texts that solely make use of SAS as the software of choice for the analysis of data are rare. While this is understandable from a marketability point of view, clearly such texts will serve the need of many thousands of students and professionals who desire to learn how to use SAS beyond the basic introduction they usually receive from taking an introductory statistics course. More recently, several authors in statistical methodology have begun to incorporate SAS in their texts but these books are limited to more specialized subjects.
Many of the standard topics covered in statistical methods texts supplemented by advanced material more suited for a second course in applied statistics are included, so that specific aspects of SAS procedures can be illustrated. Brief but instructive reviews of the statistical methodologies used are provided, and then illustrated with analysis of data sets used in well-known statistical methods texts. Particular attention is devoted to discussions of models used in each analysis because the authors believe that it is important for users to have not only an understanding of how these models are represented in SAS but also because it helps in the interpretation of the SAS output produced.
Introduction to SAS language. - More on SAS programming and some applications. - Statistical graphics using SAS/GRAPH. - Statistical analysis of regression models. - Analysis of variance models. - Analysis of variance: random mixed effects models. - Appendices. - References. - Index.
Series: CMS Books in Mathematics
2009, Approx. 470 p. 29 illus., Hardcover
ISBN: 978-0-387-78932-3
Due: January 2009
Mathematical analysis offers a solid ground to many achievements in applied
mathematics and discrete mathematics, and this text is aimed at advanced
undergraduates studying mathematics or computer science. The book is focused
on differential and integral calculus, and the author has made every effort
to include useful and relevant examples, exercises, and results enlightening
the reader to the power of mathematical tools.
The author provides excursions from the standard topics to modern and exciting topics, to illustrate the fact that even first or second year students can understand certain research problems.
The manuscript has been divided into ten chapters and covers topics on sets and numbers, linear spaces and metric spaces, sequences and series of numbers and of functions, limits and continuity, differential and integral calculus of functions of one or several variables, constants (mainly pi, but not only) and algorithms for finding them, the W - Z method of summation, estimates of algorithms and of certain combinatorial problems. Many challenging exercises accompany the text. Most of them have been used to prepare for different mathematical competitions during the past few years. In this respect, the author has maintained a healthy balance theory and exercises.
Preface.- Sets and Numbers.- Vector Spaces and Metric Spaces.- Sequences and Series.- Limits and Continuity.- Differential Calculus on R.- Integral Calculus.- Differential Calculus on Rn.- Double Integrals, Triple Integrals, and Line Integrals.- Constants.- Asymptotic and Combinatorial Estimates.- References.- List of Symbols.- Author Index.- Subject Index.-