42 Statistics Springer News 1/2008 springer.com/booksellers
B. C. Arnold, N. Balakrishnan, J. M. Sarabia, G. Casella, University of Florida, Gainesville, FL, USA A. DasGupta, Purdue University, West Lafayette, IN, R. Mínguez (Eds.) USA Statistical Design Advances in Mathematical Asymptotic Theory of and Statistical Modeling Statistics and Probability Statistical design is one of the fundamentals of our subject, being at the core of the growth of statistics Enrique Castillo is a leading figure in several during the previous century. Design played a key This book is an encyclopedic treatment of classic as mathematical and engineering fields, having role in agricultural statistics and set down prin- well as contemporary large sample theory, dealing contributed seminal work in such areas as Bayesian ciples of good practic, principles that still apply with both statistical problems and probabilistic networks, neural networks, functional equations, today. Statistical design is all about understanding issues and tools. It is written in an extremely artificial intelligence, linear algebra, optimization where the variance comes from, and making sure lucid style, with an emphasis on the conceptual methods, numerical methods, reliability engi- that is where the replication is. Indeed, it is prob- discussion of the importance of a problem and the neering, as well as sensitivity analysis and its appli- ably correct to say that these principles are even impact and relevance of the theorems. The book cations. Organized to honor Castillo’s significant more important today. has 34 chapters over a wide range of topics, nearly contributions, this volume is an outgrowth of the 600 exercises for practice and instruction, and “International Conference on Mathematical and Features another 300 worked out examples. It also includes Statistical Modeling,” and covers recent advances 7 Basic theoretical underpinnings are covered a large compendium of 300 useful inequalities on in mathematical and statistical modeling. 7 Describes the principles that drive good designs probability, linear algebra, and analysis that are and good statistics collected together from numerous sources, as an Features invaluable reference for researchers in statistics, 7 Contributors are prominent, distinguished, and Field of interest probability, and mathematics. well-respected researchers in the field of math- Statistical Theory and Methods ematical and statistical modeling 7 Real-world Features applications to safety, reliability, life-testing, finan- Target groups 7 Encyclopedic coverage of classical topics and cial modeling, quality control, general inference, Grad students, researchers at the same time of some of the most modern economics, engineering, as well as neural networks topics 7 Versatile research reference to anyone and computational techniques 7 Good reference Type of publication working on theoretical statistics and probability work for practitioners, researchers, and graduate Graduate/Advanced undergraduate textbook 7 Emphasis on presenting the material in a lucid students in statistics, applied mathematics, engi- and accessible style, suitable for conceptual under- neering, economics, and modeling standing of a very broad range of topics.
From the contents From the contents Part I. Distribution Theory and Applications.- Part Basic Convergence Concepts and Theorems.- II. Probability and Statistics.- Part III. Order Statis- Metrics, Information Theory, Convergence, and tics and Analysis.-Part IV. Engineering Modeling.- Poisson Approximations.- More General Weak and Part V. Extreme-Value Theory.- Part VI. Business Strong Laws and the Delta Theorem.- Transforma- and Economics Applications.- Part VII. Statistical tions.- More General Clts.- Moment Convergence Methods.- Part VIII. Applied Mathematics. and Uniform Integrability.- Sample Percentiles and Order Statistics.- Sample Extremes.- Central Limit Fields of interest theorems for Dependent Sequences.- Central Limit Statistical Theory and Methods; Statistics for Theorem for Markov Chains.- Accuracy of Clts.- Business/Economics/Mathematical Finance/Insur- Invariance Principles.- Edgeworth Expansions and ance; Statistics for Engineering, Physics, Computer Cumulants.- Saddlepoint Approximations.- Science, Chemistry & Geosciences U-Statistics.- Maximum Likelihood Estimates.- M Estimates.- The Trimmed Mean. Target groups Practitioners, researchers, and graduate students Field of interest in statistics, applied mathematics, engineering, Statistical Theory and Methods economics, and modeling Target groups Type of publication Students Contributed volume Type of publication Graduate/Advanced undergraduate textbook
Due March 2008 Due April 2008 Due April 2008 2008. Approx. 410 p. 56 illus. (Statistics for Industry and 2008. Approx. 695 p. (Springer Texts in Statistics) Technology) Hardcover 2008. XX, 290 p. (Springer Texts in Statistics) Hardcover Hardcover 7 approx. € 97,00 | £58.50 7 approx. € 65,45 | £50.50 7 € 69,95 | £54.00 9
J. D. Cryer, K. Chan, University of Iowa, Iowa City, S. M. Iacus, University of Milan, Milan, Italy M. R. Kosorok, University of North Carolina, Chapel IA, USA Hill, NC, USA Simulation and Inference Time Series Analysis with for Stochastic Differential Introduction to Empirical Applications in R Equations Processes and Semiparametric With R Examples Inference
The book was developed for a one-semester course usually attended by students in statistics, This book provides a self-contained, linear, and economics, business, engineering, and quantitative This book is very different from any other publica- unified introduction to empirical processes and social sciences. Basic applied statistics is assumed tion in the field and it is unique because of its focus semiparametric inference. These powerful research through multiple regression. Calculus is assumed on the practical implementation of the simula- techniques are surprisingly useful for developing only to the extent of minimizing sums of squares tion and estimation methods presented. The book methods of statistical inference for complex but a calculus-based introduction to statistics is should be useful to practitioners and students with models and in understanding the properties of necessary for a thorough understanding of some minimal mathematical background, but because such methods. The targeted audience includes of the theory. However, required facts concerning of the many R programs, probably also to many statisticians, biostatisticians, and other researchers expectation, variance, covariance, and correla- mathematically well educated practitioners. Many with a background in mathematical statistics tion are reviewed in appendices. Also, conditional of the methods presented in the book have, so far, who have an interest in learning about and doing expectation properties and minimum mean square not been used much in practice because the lack research in empirical processes and semipara- error prediction are developed in appendices. of an implementation in a unified framework. This metric inference but who would like to have a Actual time series data drawn from various disci- book fills the gap. With the R code included in friendly and gradual introduction to the area. The plines are used throughout the book to illustrate this book, a lot of useful methods become easy to book can be used either as a research reference the methodology. The book contains additional use for practitioners and students. An R package or as a textbook. The level of the book is suitable topics of a more advanced nature that could be called ‚sde‘ provides functions with easy interfaces for a second year graduate course in statistics or selected for inclusion in a course if the instructor ready to be used on empirical data from real life biostatistics, provided the students have had a year so chooses. applications. Although it contains a wide range of graduate level mathematical statistics and a of results, the book has an introductory character semester of probability. Features and necessarily does not cover the whole spectrum 7 Fully integrates time series theory with applica- of simulation and inference for general stochastic Features tions 7 Has an associated R package, TSA, to differential equations. 7 A self-contained, linear, and unified introduc- carry out the required computations and graphics tion to empirical processes and semiparametric 7 Uses numerous interesting real datsets to illus- Features inference 7 Homework problems are also trate all of the ideas 7 Ready-to-use functions allow for instant included at the end of each chapter analysis on real life data 7 Many figures give Contents immediate feeling on how methods perform Contents Introduction.- Fundamental Concepts.- Trends.- 7 Theoretical results are presented side-by-side Introduction.- An Overview of The Empirical Models for Stationary Time Series.- Models for with R code to ease the passage from theory to Processes.- Overview of Semiparametric Infer- Nonstationary Time Series.- Model Specification.- practice ence.- Case Studies I.- Introduction to Empirical Parameter Estimation.- Model Diagnostics.- Fore- Processes.- Preliminiaries for Empirical Processes.- casting.- Seasonal Models.- Time Series Regression Contents Stochastic Convergence.- Empirical Process Models.- Time Series Models of Heteroscedas- Stochastic processes and stochastic differential Methods.- Entropy Calculations.- Bootstrapping ticity.- Introduction to Spectral Analysis.- Esti- equations.- Numerical methods for SDE.- Para- Empirical Processes.- Additional Empirical Process mating the Spectrum.- Threshold Models. metric estimation.- Miscellaneous topics. Results.- The Functional Delta Method.- Z-Estima- tors.- M-Estimators.- Case Studies II.- Introduc- Field of interest Fields of interest tion To Semiparametric Inference.- Seimpara- Statistical Theory and Methods Statistics and Computing/Statistics Programs; metric Models and Efficiency.- Efficient Inference Econometrics; Simulation and Modeling for Fininte-Dimensional Parameters.- Efficient Target groups Inference for Infinite-Dimensional Parameters.- Gradate students Target groups Semiparametric M-Estimators.- Case Studies III. Researchers, graduate students Type of publication Field of interest Graduate/Advanced undergraduate textbook Type of publication Statistical Theory and Methods Monograph Target groups Statisticians, researchers, grad students
Type of publication Monograph
Due April 2008 Due April 2008 Due February 2008 2nd ed. 2008. XI, 483 p. (Springer Texts in Statistics) Hardcover 2008. XIII, 257 p. (Springer Series in Statistics) Hardcover 2008. XII, 476 p. (Springer Series in Statistics) Hardcover 7 € 69,95 | £54.00 7 € 64,95 | £50.00 7 € 69,95 | £54.00 9
J. S. Liu, Harvard University, Cambridge, MA, USA C. N. Morris, Harvard University, Cambridge, MA, D. Sarkar, Fred Hutchinson Cancer Research Center, USA; R. Tibshirani, Stanford University, Stanford, CA, Seattle, WA, USA Monte Carlo Strategies in USA (Eds.) Scientific Computing Lattice The Science of Bradley Efron Multivariate Data Visualization with R Selected Papers This paperback edition is a reprint of the 2001 Springer edition. R is rapidly growing in popularity as the environ- This book provides a self-contained and up-to- A collection of journal articles by renowned statis- ment of choice for data analysis and graphics date treatment of the Monte Carlo method and tician Bradley Efron. both in academia and industry. Lattice brings the develops a common framework under which proven design of Trellis graphics (originally devel- various Monte Carlo techniques can be “standard- Features oped for S by William S. Cleveland and colleagues ized” and compared. Given the interdisciplinary 7 Collects the most important papers of Bradley at Bell Labs) to R, considerably expanding its nature of the topics and a moderate prerequisite Efron in one place 7 Adds comments by Efron capabilities in the process. Lattice is a powerful for the reader, this book should be of interest to and other important statisticians and elegant high level data visualization system a broad audience of quantitative researchers such that is sufficient for most everyday graphics needs, as computational biologists, computer scientists, Contents yet flexible enough to be easily extended to handle econometricians, engineers, probabilists, and Foreword by Bradley Efron.-1. From 1965: The demands of cutting edge research. Written by the statisticians. It can also be used as the textbook for convex hull of a random set of points, Introduced author of the lattice system, this book describes a graduate-level course on Monte Carlo methods. by Tom Cover.- 2. From 1971: Forcing a sequential it in considerable depth, beginning with the Many problems discussed in the alter chapters experiment to be balanced, Introduced by Herman essentials and systematically delving into specific can be potential thesis topics for masters’ or Ph.D. Chernoff.-3. From 1975: Defining the curvature low levels details as necessary. No prior experience students in statistics or computer science depart- of a statistical problem (with applications to with lattice is required to read the book, although ments. second order efficiency) Introduced by Rob Kass basic familiarity with R is assumed. The book and Paul Vos.- 4. From 1975: Data analysis using contains close to 150 figures produced with lattice. Features Stein’s estimator and its generalizations (with Carl 7 The author is a leading researcher in a very Morris), Introduced by John Rolph.- 5. From 1976: Features active area of research 7 Emphasis is on making Estimating the number of unseen species: How 7 Gives a comprehensive overview of the Lattice these methods accessible to scientists who want to many words did Shakespeare know? (with Ronald graphics system and shows how to use it effectively apply them 7 Includes examples from artificial Thisted), Introduced by Peter McCullagh.- 6. From 7 Includes numerous examples using datat sets intelligence, computational biology, computer 1977: The efficiency of Cox’s likelihood function from various R packages 7 All codes and figures vision and chemistry for censored data, Introduced by John Kalbfleisch.- including color version also available online 7. From 1977: Stein’s paradox in statistics (with From the contents Carl Morris), Introduced by Jim Berger.- 8. From Contents Basic Principles: Rejection, Weighting, and 1978: Assessing the accuracy of the maximum like- Introduction.- The anatomy of a Trellis display.- Others.- Theory of Sequential Monte Carlo.- lihood estimator: Observed versus expected Fisher Visualizing univariate distributions.- Displaing Sequential Monte Carlo in Action.- Metropolis information (with David V. Hinkley), Introduced multiway tables.- Scatter plots and exten- Algorithm and Beyond.- The Gibbs Sampler.- by Thomas DiCiccio.- 9. From 1979: Bootstrap sions.- Trivariate displays.- Specialized displays.- Cluster Algorithms for the Ising Model.- General methods: Another look at the jackknife, Intro- Scales.- Annotation.- Devices, themes and other Conditional Sampling.- Molecular Dynamics duced by David Hinkley .- 10. From 1981: The options.- Conditioning.- Manipulating the Trellis and Hybrid Monte Carlo.- Multilevel Sampling jackknife estimate of variance (with Charles Stein), object.- Augmenting Trellis displays.- Writing and Optimization Methods.- Population-Based Introduced by Jun Shao and C.F. Jeff Wu. panel functions.- Creating new Trellis displays.- Monte Carlo Methods.- Markov Chains and Their A few tips and tricks. Convergence.- Selected Theoretical Topics.- Basics Fields of interest in Probability and Statistics. Statistical Theory and Methods Fields of interest Statistics and Computing/Statistics Programs; Fields of interest Target groups Visualization; Computer Graphics Statistical Theory and Methods; Computational Statisticians Mathematics and Numerical Analysis; Statistics for Target groups Business/Economics/Mathematical Finance/Insur- Type of publication Researchers, grad students ance Monograph Type of publication Target groups Monograph Researchers, graduate students
Type of publication Monograph Due March 2008
Due February 2008 Only available in print Due April 2008 2008. Approx. 360 p. (Springer Series in Statistics) 2008. Approx. 520 p. (Springer Series in Statistics / Softcover Perspectives in Statistics) Hardcover 2008. Approx. 290 p. 8 illus. in color. (Use R) Softcover 7 € 39,95 | £30.50 7 € 119,95 | £92.50 7 € 44,95 | £34.50 9