Series: Springer Texts in Statistics
2008, 494 p., Hardcover
ISBN: 978-0-387-75958-6
Due: April 2008
About this textbook
Time Series Analysis With Applications in R, Second Edition, presents an accessible approach to understanding time series models and their applications. Although the emphasis is on time domain ARIMA models and their analysis, the new edition devotes two chapters to the frequency domain and three to time series regression models, models for heteroscedasticty, and threshold models. All of the ideas and methods are illustrated with both real and simulated data sets.
A unique feature of this edition is its integration with the R computing environment. The tables and graphical displays are accompanied by the R commands used to produce them. An extensive R package, TSA, which contains many new or revised R functions and all of the data used in the book, accompanies the written text. Script files of R commands for each chapter are available for download. There is also an extensive appendix in the book that leads the reader through the use of R commands and the new R package to carry out the analyses.
Jonathan Cryer is Professor Emeritus, University of Iowa, in the Department of Statistics and Actuarial Science. He is a Fellow of the American Statistical Association and received a Collegiate Teaching Award from the University of Iowa College of Liberal Arts and Sciences. He is the author of Statistics for Business: Data Analysis and Modeling, Second Edition, (with Robert B. Miller), the Minitab Handbook, Fifth Edition, (with Barbara Ryan and Brian Joiner), the Electronic Companion to Statistics (with George Cobb), Electronic Companion to Business Statistics (with George Cobb) and numerous research papers.
Kung-Sik Chan is Professor, University of Iowa, in the Department of Statistics and Actuarial Science. He is a Fellow of the American Statistical Association and the Institute of the Mathematical Statistics, and an Elected Member of the International Statistical Institute. He received a Faculty Scholar Award from the University of Iowa in 1996. He is the author of Chaos: A Statistical Perspective (with Howell Tong) and numerous research papers.
Table of contents
Introduction.- Fundamental Concepts.- Trends.- Models for Stationary Time Series.- Models for Nonstationary Time Series.- Model Specification.- Parameter Estimation.- Model Diagnostics.- Forecasting.- Seasonal Models.- Time Series Regression Models.- Time Series Models of Heteroscedasticity.- Introduction to Spectral Analysis.- Estimating the Spectrum.- Threshold Models.
Series: Springer Texts in Statistics
2008, Approx. 695 p., Hardcover
ISBN: 978-0-387-75970-8
Due: April 2008
About this textbook
This book is an encyclopedic treatment of classic as well as contemporary large sample theory, dealing with both statistical problems and probabilistic issues and tools. It is written in an extremely lucid style, with an emphasis on the conceptual discussion of the importance of a problem and the impact and relevance of the theorems. The book has 34 chapters over a wide range of topics, nearly 600 exercises for practice and instruction, and another 300 worked out examples. It also includes a large compendium of 300 useful inequalities on probability, linear algebra, and analysis that are collected together from numerous sources, as an invaluable reference for researchers in statistics, probability, and mathematics.
It can be used as a graduate text, as a versatile research reference, as a source for independent reading on a wide assembly of topics, and as a window to learning the latest developments in contemporary topics. The book is unique in its detailed coverage of fundamental topics such as central limit theorems in numerous setups, likelihood based methods, goodness of fit, higher order asymptotics, as well as of the most modern topics such as the bootstrap, dependent data, Bayesian asymptotics, nonparametric density estimation, mixture models, and multiple testing and false discovery. It provides extensive bibliographic references on all topics that include very recent publications.
Anirban DasGupta is Professor of Statistics at Purdue University. He has also taught at the Wharton School of the University of Pennsylvania, at Cornell University, and at the University of California at San Diego. He has been on the editorial board of the Annals of Statistics since 1998 and has also served on the editorial boards of the Journal of the American Statistical Association, International Statistical Review, and the Journal of Statistical Planning and Inference. He has edited two monographs in the lecture notes monograph series of the Institute of Mathematical Statistics, is a Fellow of the Institute of Mathematical Statistics and has 70 refereed publications on theoretical statistics and probability in major journals.
Table of contents
Basic Convergence Concepts and Theorems.- Metrics, Information Theory, Convergence, and Poisson Approximations.- More General Weak and Strong Laws and the Delta Theorem.- Transformations.- More General Clts.- Moment Convergence and Uniform Integrability.- Sample Percentiles and Order Statistics.- Sample Extremes.- Central Limit theorems for Dependent Sequences.- Central Limit Theorem for Markov Chains.- Accuracy of Clts.- Invariance Principles.- Edgeworth Expansions and Cumulants.- Saddlepoint Approximations.- U-Statistics.- Maximum Likelihood Estimates.- M Estimates.- the Trimmed Mean.- Multivariate Location Parameter and Multivariate Medians.- Bayes Procedures and Posterior Distributions.- Testing Problems.- Asymptotic Efficiency in Testing.- Some General Large Deviation Results.- Classical Nonparametrics.- Two-Sample Problems.- Goodness of Fit.- Chi-Square Tests for Goodness of Fit.- Goodness of Fit With Estimated Parameters.- The Bootstrap.- Jackknife.- Permutation Tests.- Density Estimation.- Mixture Models and Nonparametric Deconvolution.- High Dimensional Inference and False Discovery
Series: Springer Series in Statistics
2008, XIII, 257 p., Hardcover
ISBN: 978-0-387-75838-1
Due: April 2008
About this book
This book is very different from any other publication in the field and it is unique because of its focus on the practical implementation of the simulation and estimation methods presented. The book should be useful to practitioners and students with minimal mathematical background, but because of the many R programs, probably also to many mathematically well educated practitioners. Many of the methods presented in the book have, so far, not been used much in practice because the lack of an implementation in a unified framework. This book fills the gap. With the R code included in this book, a lot of useful methods become easy to use for practitioners and students. An R package called 'sde' provides functions with easy interfaces ready to be used on empirical data from real life applications. Although it contains a wide range of results, the book has an introductory character and necessarily does not cover the whole spectrum of simulation and inference for general stochastic differential equations.
The book is organized in four chapters. The first one introduces the subject and presents several classes of processes used in many fields of mathematics, computational biology, finance and the social sciences. The second chapter is devoted to simulation schemes and covers new methods not available in other milestones publication known so far. The third one is focused on parametric estimation techniques. In particular, it includes exact likelihood inference, approximated and pseudo-likelihood methods, estimating functions, generalized method of moments and other techniques. The last chapter contains miscellaneous topics like nonparametric estimation, model identification and change point estimation. The reader non-expert in R language, will find a concise introduction to this environment focused on the subject of the book which should allow for instant use of the proposed material. To each R functions presented in the book a documentation page is available at the end of the book.
Stefano M. Iacus is associate professor of Probability and Mathematical Statistics at the University of Milan, Department of Economics, Business and Statistics. He has a PhD in Statistics at Padua University, Italy and in Mathematics at Universite du Maine, France. He is a member of the R Core team for the development of the R statistical environment, Data Base editor for the Current Index to Statistics, IMS Group Manager for the Institute of Mathematical Statistics and member of several societies. He has been associate editor of Journal of Statistical Software.
Table of contents
Stochastic processes and stochastic differential equations.- Numerical methods for SDE.- Parametric estimation.- Miscellaneous topics.
Series: Use R
2008, Approx. 290 p. 8 illus. in color., Softcover
ISBN: 978-0-387-75968-5
Due: April 2008
About this book
R is rapidly growing in popularity as the environment of choice for data analysis and graphics both in academia and industry. Lattice brings the proven design of Trellis graphics (originally developed for S by William S. Cleveland and colleagues at Bell Labs) to R, considerably expanding its capabilities in the process. Lattice is a powerful and elegant high level data visualization system that is sufficient for most everyday graphics needs, yet flexible enough to be easily extended to handle demands of cutting edge research. Written by the author of the lattice system, this book describes it in considerable depth, beginning with the essentials and systematically delving into specific low levels details as necessary. No prior experience with lattice is required to read the book, although basic familiarity with R is assumed.
The book contains close to150 figures produced with lattice. Many of the examples emphasize principles of good graphical design; almost all use real data sets that are publicly available in various R packages. All code and figures in the book are also available online, along with supplementary material covering more advanced topics. Deepayan Sarkar won the 2004 John M. Chambers Statistical Software Award for writing lattice while he was a graduate student in Statistics at the University of Wisconsin-Madison. He is currently doing postdoctoral research in the Computational Biology program at the Fred Hutchinson Cancer Research Center, a member of the R Core Team, and an active participant on the R mailing lists.
Table of contents
Introduction.- A technical overview of lattice.- Visualizing univariate distributions.- Displaying multiway tables.- Scatter plots and extensions.- Trivariate displays.- Graphical parameters and other settings.- Plot coordinates and axis annotation.- Labels and legends.- Data manipulation and related topics.- Manipulating the "trellis" object.- Interacting with Trellis displays.- Advanced panel functions.- New Trellis displays.
Series: Ergebnisse der Mathematik und ihrer Grenzgebiete. 3. Folge
/ A Series of Modern Surveys in Mathematics , Preliminary entry 51
2008, Hardcover
ISBN: 978-3-540-77262-0
Due: April 2008
About this book
The aim of this monograph is to give an overview of various classes of infinite-dimensional Lie groups and their applications, mostly in Hamiltonian mechanics, fluid dynamics, integrable systems, and complex geometry. The authors have chosen to present the unifying ideas of the theory by concentrating on specific types and examples of inifinite-dimensional Lie groups.
Infinite-dimensional Lie groups arise naturally in many places in mathematics and physics, e.g. as symmetries of various evolution equations or gauge theories. Their applications range from quantum mechanics to meteorology: such groups as the groups of diffeomorphisms, of differential and integral operators, groups of gauge transformations play a role in problems related to differential and algebraic geometry, knot theory, string theory, fluid dynamics and cosmology. Although infinite-dimensional Lie groups have been investigated for quite some time, the scope of applicability of a general theory of such groups is still rather limited. The main reason for this is that infinite-dimensional Lie groups exhibit very peculiar features.
Table of contents
Preface.- Introduction.- I Preliminaries.- II Infinite-dimensional Lie Groups: Their Geometry, Orbits and Dynamical Systems.- III Applications of Groups: Topological and Holomorphic Gauge Theories.- Appendices.- A1 Root Systems.- A2 Compact Lie Groups.- A3 Krichever-Novikov Algebras.- A4 Kahler Structures on the Virasoro and Loop Group Coadjoint Orbits.- A5 Metrics and Diameters of the Group of Hamiltonian Diffeomorphisms.- A6 Semi-Direct Extensions of the Diffeomorphism Group and Gas Dynamics.- A7 The Drinfeld-Sokolov Reduction.- A8 Surjectivity of the Exponential Map on Pseudo-Differential Symbols.- A9 Torus Actions on the Moduli Space of Flat Connections.- Bibliography.- Index