Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. By complementing the exclusive focus of classical least squares regression on the conditional mean, quantile regression offers a systematic strategy for examining how covariates influence the location, scale and shape of the entire response distribution. This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric. The author has devoted more than 25 years of research to this topic. The methods in the analysis are illustrated with a variety of applications from economics, biology, ecology and finance. The treatment will find its core audiences in econometrics, statistics, and applied mathematics in addition to the disciplines cited above.

Classical statistical techniques fail to cope well with deviations from a standard distribution. Robust statistical methods take into account these deviations while estimating the parameters of parametric models, thus increasing the accuracy of the inference. Research into robust methods is flourishing, with new methods being developed and different applications considered. Robust Statistics sets out to explain the use of robust methods and their theoretical justification. It provides an up-to-date overview of the theory and practical application of the robust statistical methods in regression, multivariate analysis, generalized linear models and time series. This unique book: * Enables the reader to select and use the most appropriate robust method for their particular statistical model. * Features computational algorithms for the core methods. * Covers regression methods for data mining applications. * Includes examples with real data and applications using the S-Plus robust statistics library. * Describes the theoretical and operational aspects of robust methods separately, so the reader can choose to focus on one or the other. * Supported by a supplementary website featuring time-limited S-Plus download, along with datasets and S-Plus code to allow the reader to reproduce the examples given in the book. Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is ideal for researchers, practitioners and graduate students of statistics, electrical, chemical and biochemical engineering, and computer vision. There is also much to benefit researchers from other sciences, such as biotechnology, who need to use robust statistical methods in their work.

The study of copulas and their role in statistics is a new but vigorously growing field. In this book the student or practitioner of statistics and probability will find discussions of the fundamental properties of copulas and some of their primary applications. The applications include the study of dependence and measures of association, and the construction of families of bivariate distributions. This book is suitable as a text or for self-study.

"A reader's first impression on leafing through this book is of the large number of graphs and diagrams, used to illustrate shapes of distributions...and to show real data examples in various ways. A closer reading reveals a nice mix of theory and applications, with the copious graphical illustrations alluded to. Such a mixture is of course dear to the heart of the applied probabilist/statistician, and should impress even the most ardent theorists." --MATHEMATICAL REVIEWS

The field of statistics is rapidly transforming into a discipline that Hal Varian at Google has called "sexy". And with good reason - from batting averages and political polls to game shows and medical research - the real-world application of statistics is growing by leaps and bounds. In Naked Statistics, Charles Wheelan strips away the arcane and technical details to get at the underlying intuition that is key to understanding the power of statistical concepts. Tackling a wide-ranging set of problems, he demonstrates how statistics can be used to look at questions that are important and relevant to us today. With the trademark wit, accessibility and fun that made Naked Economics a bestseller, Wheelan brings another essential discipline to life with a one-in-a-million statistics book that you will read for pleasure.

This classic book uses words and diagrams, rather than formulas and equations, to help readers understand what statistics is, and how to think statistically. It focuses on the ideas behind statistics only; readers are not required to perform any calculations.

If you have ever looked for P-values by shopping at P mart, tried to watch the Bernoulli Trials on "People's Court," or think that the standard deviation is a criminal offense in six states, then you need The Cartoon Guide to Statistics to put you on the road to statistical literacy. The Cartoon Guide to Statistics covers all the central ideas of modern statistics: the summary and display of data, probability in gambling and medicine, random variables, Bernoulli Trails, the Central Limit Theorem, hypothesis testing, confidence interval estimation, and much more--all explained in simple, clear, and yes, funny illustrations. Never again will you order the Poisson Distribution in a French restaurant!

Designed for self-instruction, this text is intended for students to use on their own while simultaneously taking a statistics course using a standard textbook. Then on mathematical approach maximizes the use of verbal and visual languages. The text covers such topics as Bayes' Theorem and statistical independence, probability distributions, confidence intervals, and analysis of variance.

This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkison''s Grammar of Graphics to create a powerful and flexible system for creating data graphics. With ggplot2, it''s easy to:
* produce handsome, publication-quality plots, with automatic legends created from the plot specification
* superpose multiple layers (points, lines, maps, tiles, box plots to name a few) from different data sources, with automatically adjusted common scales
* add customisable smoothers that use the powerful modelling capabilities of R, such as loess, linear models, generalised additive models and robust regression
* save any ggplot2 plot (or part thereof) for later modification or reuse
* create custom themes that capture in-house or journal style requirements, and that can easily be applied to multiple plots
* approach your graph from a visual perspective, thinking about how each component of the data is represented on the final plot.
This book will be useful to everyone who has struggled with displaying their data in an informative and attractive way. You will need some basic knowledge of R (i.e. you should be able to get your data into R), but ggplot2 is a mini-language specifically tailored for producing graphics, and you''ll learn everything you need in the book. After reading this book you''ll be able to produce graphics customized precisely for your problems, and you''ll find it easy to get graphics out of your head and on to the screen or page.

In this new edition the author has added substantial material on Bayesian analysis, including lengthy new sections on such important topics as empirical and hierarchical Bayes analysis, Bayesian calculation, Bayesian communication, and group decision making. With these changes, the book can be used as a self-contained introduction to Bayesian analysis. In addition, much of the decision-theoretic portion of the text was updated, including new sections covering such modern topics as minimax multivariate (Stein) estimation.

This is an introduction to Bayesian statistics and decision theory, including advanced topics such as Monte Carlo methods. This new edition contains several revised chapters and a new chapter on model choice.

An excellent introduction for electrical, electronics engineers and computer scientists who would like to have a good, basic understanding of the stochastic processes! This clearly written book responds to the increasing interest in the study of systems that vary in time in a random manner. It presents an introductory account of some of the important topics in the theory of the mathematical models of such systems. The selected topics are conceptually interesting and have fruitful application in various branches of science and technology.

Useful as a text for students and a reference for the more advanced mathematician, this book presents a unified treatment of that part of measure theory most useful for its application in modern analysis. Coverage includes sets and classes, measures and outer measures, Haar measure and measure and topology in groups. From the reviews: "Will serve the interested student to find his way to active and creative work in the field of Hilbert space theory." --MATHEMATICAL REVIEWS

Markov chains are central to the understanding of random processes. This is not only because they pervade the applications of random processes, but also because one can calculate explicitly many quantities of interest. This textbook, aimed at advanced undergraduate or MSc students with some background in basic probability theory, focuses on Markov chains and quickly develops a coherent and rigorous theory whilst showing also how actually to apply it. Both discrete-time and continuous-time chains are studied. A distinguishing feature is an introduction to more advanced topics such as martingales and potentials in the established context of Markov chains. There are applications to simulation, economics, optimal control, genetics, queues and many other topics, and exercises and examples drawn both from theory and practice. It will therefore be an ideal text either for elementary courses on random processes or those that are more oriented towards applications.

WINNER OF THE 2005 DEGROOT PRIZE! This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level.