Series: Use R
2008, Approx. 410 p., Softcover
ISBN: 978-0-387-78170-9
Due: August 2008
- First comprehensive introduction to applied spatial data analysis with R.
- Extensive practical examples, with data sets, allowing the reader to follow the presentation in a hands-on way.
-Brings the reader up to speed quickly, facilitating rapid research project completion.
- Gives the reader control and understanding of applied researcher with spatial data.
Applied Spatial Data Analysis with R is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website.
This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information systems, the environmental sciences, ecology, public health and disease control, economics, public administration and political science.
The book has a website where coloured figures, complete code examples, data sets, and other support material may be found: http://www.asdar-book.org.
The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.
Roger Bivand is Professor of Geography in the Department of Economics at Norges Handelshoyskole, Bergen, Norway. Edzer Pebesma is Professor of Geoinformatics at Westfalische Wilhelms-Universitat, Munster, Germany. Virgilio Gomez-Rubio is Research Associate in the Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom.
Hello, world: handling spatial data in R.- Classes for spatial data in R.- Visualizing spatial data.- Spatial data import and export.- Further methods for handling spatial data.- Customising spatial data classes and methods.- Spatial point pattern analysis.- Interpolation and geostatistics.- Areal data and spatial autocorrelation.- Modelling areal data.- Disease mapping.- Afterword.- References.
Series: Universitext
2008, Approx. 510 p., Softcover
ISBN: 978-88-470-0875-5
Due: August 2008
The purpose of the volume is to provide a support for a first corse in Mathematical Analysis, along the lines of the recent Programme Specifications for mathematical teaching in European universities. The contents are organised to appeal especially to Engineering, Physics and Computer Science students, all areas in which mathematical tools play a crucial role. Basic notions and methods of differential and integral calculus for functions of one real variable are presented in a manner that elicits critical reading and prompts a hands-on approach to concrete applications. The layout has a specifically-designed modular nature, allowing the instructor to make flexible didactical choices when planning an introductory lecture course. The book may in fact be employed at three levels of depth. At the elementary level the student is supposed to grasp the very essential ideas and familiarise with the corresponding key techniques. Proofs to the main results befit the intermediate level, together with several remarks and complementary notes enhancing the treatise. The last, and farthest-reaching, level consists of links to online material, which enable the strongly motivated reader to explore further into the subject. Definitions and properties are furnished with substantial examples to stimulate the learning process. Over 350 solved exercises complete the text, at least half of which guide the reader to the solution.
Engineering, Physics and Computer Science students
Mathematical Analysis
PDE
differential and integral calculus
2008, XVI, 634 p., Softcover
ISBN: 978-0-387-78356-7
Due: August 2008
Provides essential background in matrix algebra, a prerequisite to research and understanding in these areas
Self-contained work that is ideal for readers who have had previous experience of matrices
Solutions to the exercises are available in the author's Matrix Algebra: Exercises and Solutions
This book presents matrix algebra in a way that is well-suited for those with an interest in statistics or a related discipline. It provides thorough and unified coverage of the fundamental concepts along with the specialized topics encountered in areas of statistics such as linear statistical models and multivariate analysis. It includes a number of very useful results that have only been available from relatively obscure sources. Detailed proofs are provided for all results. The style and level of presentation are designed to make the contents accessible to a broad audience. The book is essentially self-contained, though it is best-suited for a reader who has had some previous exposure to matrices (of the kind that might be acquired in a beginning course on linear or matrix algebra). It includes exercises, it can serve as the primary text for a course on matrices or as a supplementary text in courses on such topics as linear statistical models or multivariate analysis, and it will be a valuable reference.
David A. Harville is a research staff member emeritus in the Mathematical Sciences Department of the IBM T.J. Watson Research Center. Prior to joining the Research Center, he spent ten years as a mathematical statistician in the Applied Mathematics Research Laboratory of the Aerospace Research Laboratories (at Wright-Patterson, Air Force Base, Ohio), followed by twenty years as a full professor in the Department of Statistics at Iowa State University. He has extensive experience in the area of linear statistical models, having taught (on numberous occasions) M.S.- and Ph.D.-level courses on that topic, having been the thesis adviser of ten Ph.D. students, and having authored more than 70 research articles. His work has been recognized by his having been named a Fellow of the American Statistical Association and of the Institute of Mathematical Statistics, by his election as a member of the International Statistical Institute, and by his having served as an associate editor of Biometrics and of the Journal of the American Statistical Association.
Preface. - Matrices. - Submatrices and partitioned matricies. - Linear dependence and independence. - Linear spaces: row and column spaces. - Trace of a (square) matrix. - Geometrical considerations. - Linear systems: consistency and compatability. - Inverse matrices. - Generalized inverses. - Indepotent matrices. - Linear systems: solutions. - Projections and projection matrices. - Determinants. - Linear, bilinear, and quadratic forms. - Matrix differentiation. - Kronecker products and the vec and vech operators. - Intersections and sums of subspaces. - Sums (and differences) of matrices. - Minimzation of a second-degree polynomial (in n variables) subject to linear constraints. - The Moore-Penrose inverse. - Eigenvalues and Eigenvectors. - Linear transformations. - References. - Index.
Series: Use R
2008, Approx. 270 p., Softcover
ISBN: 978-0-387-75960-9
Due: August 2008
First book to address the area with this breadth (although the book is designed to be compact)
Describes new topics and presents multiscale as a unifying force able to be used in many different kinds of interesting problems
Wavelet methods have recently undergone a rapid period of development with important implications for a number of disciplines including statistics. This book has three main objectives: (i) providing an introduction to wavelets and their uses in statistics; (ii) acting as a quick and broad reference to many developments in the area; (iii) interspersing R code that enables the reader to learn the methods, to carry out their own analyses, and further develop their own ideas. The book code is designed to work with the freeware R package WaveThresh4, but the book can be read independently of R.
The book introduces the wavelet transform by starting with the simple Haar wavelet transform, and then builds to consider more general wavelets, complex-valued wavelets, non-decimated transforms, multidimensional wavelets, multiple wavelets, wavelet packets, boundary handling, and initialization. Later chapters consider a variety of wavelet-based nonparametric regression methods for different noise models and designs including density estimation, hazard rate estimation, and inverse problems; the use of wavelets for stationary and non-stationary time series analysis; and how wavelets might be used for variance estimation and intensity estimation for non-Gaussian sequences.
The book is aimed both at Masters/Ph.D. students in a numerate discipline (such as statistics, mathematics, economics, engineering, computer science, and physics) and postdoctoral researchers/users interested in statistical wavelet methods.
Guy Nason is Professor of Statistics at the University of Bristol. He has been actively involved in the development of various wavelet methods in statistics since 1993. He was awarded the Royal Statistical Society’s 2001 Guy Medal in Bronze for work on wavelets in statistics. He was the author of the first, free, generally available wavelet package for statistical purposes in S and R (WaveThresh2).
Wavelets, discrete wavelet transforms, non-decimated transforms, wavelet packet transforms, lifting transforms.- Multiscale methods for denoising (wavelet shrinkage).- Locally stationary wavelet time series and texture modelling.- Multiscale variable transformations for Gaussianization and variance stabilization.- Miscellaneous topics.