A publication of the Societe Mathematique de France.
Memoires de la Societe Mathematique de France, Number: 137
2014; 135 pp; softcover
ISBN-13: 978-2-85629-788-9
Expected publication date is January 20, 2015.
The author studies families of objects in Fukaya categories, specifically ones whose deformation behaviour is prescribed by the choice of an odd degree cohomology class. This leads to invariants of symplectic manifolds, which we apply to blowups along symplectic mapping tori.
A publication of the Societe Mathematique de France, Marseilles (SMF), distributed by the AMS in the U.S., Canada, and Mexico. Orders from other countries should be sent to the SMF. Members of the SMF receive a 30% discount from list.
Graduate students and research mathematicians interested in Fukaya categories.
Introduction
Families of objects
The two-torus
Symplectic automorphisms
Symplectic mapping tori
Blowing up
Bibliography
Surveys of Modern Mathematics, Volume 9
Published: 10 October 2014
Paperback
602 pages
This monograph considers the classical compressible Euler Equations in three space dimensions with an arbitrary equation of state, and whose initial data corresponds to a constant state outside a sphere. Under suitable restriction on the size of the initial departure from the constant state, the authors establish theorems which give a complete description of the maximal development. In particular, the boundary of the domain of the maximal solution contains a singular part where the density of the wave fronts blows up and shocks form. The authors obtain a detailed description of the geometry of this singular boundary, and a detailed analysis of the behavior of the solution there. The approach is geometric, the central concept being that of the acoustical spacetime manifold.
Compared to a previous monograph treating the relativistic fluids by the first author, the present monograph not only gives simpler and self-contained proofs but also sharpens some of the results. In addition, it explains in depth the ideas on which the approach is based. Moreover, certain geometric aspects which pertain only to the non-relativistic theory are discussed.
Compressible Flow and Euler’s Equations will be of interest to scholars working in partial differential equations in general and in fluid mechanics in particular.
This volume is part of the Surveys of Modern Mathematics book series.
Table of Contents (PDF)
Preview (PDF)
Publications
Advanced Lectures in Mathematics, Volume 30
Published: 22 October 2014
Paperback
218 pages
This is a collection of lecture notes from the conference CIMPA-UNESCO-CHINA Research School 2010: Automorphic forms and L-functions, held at the Weihai campus of Shandong University, China in August 2010. Included are lectures given by J. Cogdell, G. Harcos, Xiaoqing Li, P. Michel, A. Reznikov, F. Shahidi, and Yangbo Ye.
These lectures provide a helpful introduction to automorphic forms and L-functions, and to the arithmetic applications thereof.
ISBN: 978-1-118-73003-4
424 pages
December 2014
A valuable overview of the most important ideas and results in statistical analysis
Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linear statistical models. The book presents a broad, in-depth overview of the most commonly used statistical models by discussing the theory underlying the models, R software applications, and examples with crafted models to elucidate key ideas and promote practical model building.
The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations of Linear and Generalized Linear Models also features:
An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods
An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high-dimensional problems
Numerous examples that use R software for all text data analyses
More than 400 exercises for readers to practice and extend the theory, methods, and data analysis
A supplementary website with datasets for the examples and exercises
An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.
ISBN: 978-1-118-74511-3
648 pages
January 2015
Praise for the First Edition
"…[t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics." -MAA Reviews
Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze time-oriented data and construct real-world short- to medium-term statistical forecasts.
Authored by highly-experienced academics and professionals in engineering statistics, the Second Edition features discussions on both popular and modern time series methodologies as well as an introduction to Bayesian methods in forecasting. Introduction to Time Series Analysis and Forecasting, Second Edition also includes:
Over 300 exercises from diverse disciplines including health care, environmental studies, engineering, and finance
More than 50 programming algorithms using JMP, SAS, and R that illustrate the theory and practicality of forecasting techniques in the context of time- oriented data
New material on frequency domain and spatial temporal data analysis
Expanded coverage of the variogram and spectrum with applications as well as transfer and intervention model functions
A supplementary website featuring PowerPoint slides, data sets, and select solutions to the problems
Introduction to Time Series Analysis and Forecasting, Second Edition is an ideal textbook upper-undergraduate and graduate-levels courses in forecasting and time series. The book is also an excellent reference for practitioners and researchers who need to model and analyze time series data to generate forecasts.
ISBN: 978-1-118-85398-6
320 pages
April 2015
Applied mathematics and computational science play a fundamental role in new discoveries in the sciences and engineering. The methodology of mathematical modeling and computational experiment provides a primary and ubiquitous tool in such discoveries, as well as in the development of new theories and techniques for the solution of important problems arising from the sciences and engineering. This book provides readers with state-of-the-art achievements in the development of this methodology and the associated theories and techniques in diverse areas of human knowledge, promoting interdisciplinary interactions between mathematicians, scientists, and engineers. The book is a valuable source of the methods, ideas, and tools of applied and computational mathematics developed for other disciplines, including natural and social sciences, engineering, and technology.
ISBN: 978-0-471-69755-8
368 pages
April 2015
Clarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods
Featuring a thoroughly revised presentation, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the Second Edition presents additional clarification of theoretical opportunities, new algorithms, and up-to-date coverage of the unique challenges presented in the field of data analysis.
The new edition focuses on the various density estimation techniques and methods that can be used in the field of big data. Defining optimal nonparametric estimators, the Second Edition demonstrates the density estimation tools to use when dealing with various multivariate structures in univariate, bivariate, trivariate, and quadrivariate data analysis. Continuing to illustrate the major concepts in the context of the classical histogram, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition features:
Over 150 updated figures to clarify theoretical results and to show analyses of real data sets
An updated presentation of graphic visualization using computer software such as R
A clear discussion of selections of important research during the past decade, including mixture estimation, robust parametric modeling algorithms, and clustering
Over 130 problems to help readers reinforce the main concepts and ideas presented
Boxed theorems and results allowing easy identification of crucial ideas
Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition is an ideal reference for theoretical and applied statisticians, practicing engineers, as well as all readers interested in the theoretical aspects of nonparametric estimation and the application of these methods to multivariate data. The Second Edition is also a useful as a textbook for introductory courses in kernel statistics, smoothing, advanced computational statistics, and general forms of statistical distributions.