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<HTLIMH=gegcfh>ISBN 978-0-8176-4625-7 9<HTLDTH=hfjgeh>ISBN 978-0-387-75964-7 9<HTLDTH=hfjhai>ISBN 978-0-387-75970-8 springer.com/booksellers Springer News 1/2008 Statistics 43 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