Ramirez Galarza, Ana Irene, Seade, Jose
Introduction to Classical Geometries
2006, Approx. 260 p., Softcover
ISBN: 3-7643-7517-5
Due: October 2006
About this textbook
This book follows Kleinfs proposal of studying geometry by
looking at the symmetries (or rigid motions) of the space in
question. In this way the classical geometries are studied:
Euclidean, affine, elliptic, projective and hyperbolic. For
simplicity the focus is on the two-dimensional case, which is
already rich enough, though some aspects of the 3 or $n$-dimensional
geometries are included. Once plane geometry is well understood,
it is much easier to go into higher dimensions.
The book appeals to, and develops, the geometric intuition of the
reader. Some basic notions of algebra and analysis are also used
to get better understandings of various concepts and results.
Table of contents
Preface.- 1. Euclidean Geometry.- 2. Affine Geometry.- 3.
Projective Geometry.- 4. Hyperbolic Geometry.- Appendices.-
Bibliography.- Index.
Shaked, Moshe, Shanthikumar, J. George
Stochastic Orders
Series: Springer Series in Statistics
2006, XV, 481 p., Hardcover
ISBN: 0-387-32915-3
Due: October 2006
About this book
Stochastic ordering is a fundamental guide for decision making
under uncertainty. It is also an essential tool in the study of
structural properties of complex stochastic systems. This
reference text presents a comprehensive coverage of the various
notions of stochastic orderings, their closure properties, and
their applications. Some of these orderings are routinely used in
many applications in economics, finance, insurance, management
science, operations research, statistics, and various other
fields of study. And the value of the other notions of stochastic
orderings still needs to be explored further.
This book is an ideal reference for anyone interested in decision
making under uncertainty and interested in the analysis of
complex stochastic systems. It is suitable as a text for advanced
graduate course on stochastic ordering and applications.
Table of contents
Univariate stochastic orders.- Mean residual life orders.-
Univariate variability orders.- Univariate monotone convex and
related orders.- The Laplace transform and related orders.-
Multivariate stochastic orders.- Multivariate variability and
related orders.- Stochastic convexity and concavity.- Postive
dependence orders.
Xiao, Jie
Geometric Qp Functions
Series: Frontiers in Mathematics
2006, Approx. 260 p., Softcover
ISBN: 3-7643-7762-3
Due: October 2006
About this book
This book documents the rich structure of the holomorphic Q
function spaces which are geometric in the sense that they
transform naturally under conformal mappings, with particular
emphasis on the last few years' development based on interaction
between geometric function and measure theory and other branches
of mathematical analysis, including potential theory, harmonic
analysis, functional analysis, and operator theory.
Table of contents
Preface.- Preliminaries.- Poisson versus Berezin with
Generalizations.- Isomorphism, Decomposition and Discreteness.-
Invariant Preduality through Hausdorff Capacity.- Cauchy Pairing
with Expressions and Extremes.- As Symbols of Hankel and Volterra
Operators.- Estimates for Growth and Decay.- Q-Classes on
Hyperbolic Riemann Surfaces.- References.- Index.
Herman, Gabor T.; Kuba, Attila (Eds.)
Advances in Discrete Tomography and its Applications
Series: Applied and Numerical Harmonic Analysis
2007, Approx. 455 p., 125 illus., Hardcover
ISBN: 0-8176-3614-5
Due: December 2006
About this book
This book is a unified presentation of new methods, algorithms,
and select applications that are the foundations of
multidimensional image construction and reconstruction. The self-contained
survey chapters, written by leading mathematicians, engineers,
and computer scientists, present cutting-edge research and
results in the field. Three main areas are covered: theoretical
results, algorithms, and practical applications. Following an
historical and introductory overview of the field, the book
explores the various mathematical and computational problems of
discrete tomography with an emphasis on new applications.
Written for:
Professionals, researchers, practitioners, students in applied
mathematics, computer imaging, biomedical imaging, computer
engineering, image processing
Diggle, Peter J., Ribeiro, Paulo Justiniano
Model-based Geostatistics
Series: Springer Series in Statistics
2006, X, 230 p., Hardcover
ISBN: 0-387-32907-2
Due: December 2006
About this book
Geostatistics is concerned with estimation and prediction
problems for spatially continuous phenomena, using data obtained
at a limited number of spatial locations. The name reflects its
origins in mineral exploration, but the methods are now used in a
wide range of settings including public health and the physical
and environmental sciences. Model-based geostatistics refers to
the application of general statistical principles of modeling and
inference to geostatistical problems. This volume is the first
book-length treatment of model-based geostatistics.
The authors have written an expository text, emphasizing
statistical methods and applications rather than the underlying
mathematical theory. Analyses of datasets from a range of
scientific contexts feature prominently, and simulations are used
to illustrate theoretical results. Readers can reproduce most of
the computational results in the book by using the authors' R-based
software package, geoR, whose usage is illustrated in a
computation section at the end of each chapter.
The book assumes a working knowledge of classical and Bayesian
methods of inference, linear models, and generalized linear
models, but does not require previous exposure to spatial
statistical models or methods. The authors have used the material
in MSc-level statistics courses.
Peter Diggle is Professor of Statistics at Lancaster University
and Adjunct Professor of Biostatistics at Johns Hopkins
University School of Public Health. Paulo Ribeiro is Senior
Lecture at Universidade Federal do Parana.
Table of contents
Introduction.- An Overview of Model-Based Geostatistics.-
Gaussian Models for Geostatistical Data.- Generalized Linear
Models for Geostatistical Data.- Classical Parameter Estimation.-
Spatial Prediction.- Bayesian Inference.- Geostatistical Design