Bøger i Chapman & Hall/CRC Texts in Statistical Science serien

This book is intended as a text for a two-quarter or two-semester post-calculus introduction to probability and mathematical statistics for undergraduate students in their junior or senior year, and also for graduate students in the quantitative sciences (e.g., agriculture, computer science, ecology, economics, engineering, epidemiology, genetics, psychology, and many others). The book designed to effectively serve two different audiences (a) majors and minors in mathematics and statistics and (b) students in quantitative disciplines with the appropriate mathematical background and with a serious interest of understanding probability and statistics at the foundational level.

This text emphasizes nonlinear models for a course in time series analysis. After introducing stochastic processes, Markov chains, Poisson processes, and ARMA models, the authors cover functional autoregressive, ARCH, threshold AR, and discrete time series models as well as several complementary approaches. They discuss the main limit theorems for Markov chains, useful inequalities, statistical techniques to infer model parameters, and GLMs. Moving on to HMM models, the book examines filtering and smoothing, parametric and nonparametric inference, advanced particle filtering, and numerical methods for inference.

This book takes a first step in developing a full theory of richly parameterized models, which would allow statisticians to better understand their analysis results.

Updated and expanded, this popular text focuses on the quantitative aspects of epidemiological research. It shows students how statistical principles and techniques can help solve epidemiological problems. Along with more exercises and examples using both Stata and SAS, this third edition includes a new chapter on risk scores and clinical decision rules, a new chapter on computer-intensive methods, and new sections on binomial regression models, competing risk, information criteria, propensity scoring, and splines. Supporting materials are available on the book¿s CRC Press web page.

This book teaches students how to apply nonparametric techniques to statistical data from hypotheses to regression modeling, time-to-event analysis, density estimation, and resampling methods. SAS codes for all examples are given in the text. Data sets for the exercises are available.

A modern account of categorical data analysis using the popular R software, this text covers model building and assessment methods for binary, multicategory, and count response variables and discusses fundamentals, such as odds ratio and probability estimation. The authors give detailed advice and guidelines on which procedures to use and why to use them. The text incorporates numerous examples from medicine, psychology, sports, ecology, and other areas, along with extensive R code and output. Data sets, R programs, and in-class recorded videos are available on the book's website.

A major tool for quality control and management, statistical process control (SPC) monitors sequential processes, such as production lines and Internet traffic, to ensure that they work stably and satisfactorily. Along with covering traditional methods, this book describes many recent SPC methods that improve upon the more established techniques. The author¿a leading researcher on SPC¿shows how these methods can handle new applications. Pseudo codes are presented for important methods and all R functions and datasets are available on the author¿s website.

This publication provides an insight into modern developments in statistical methodology using examples that highlight connections between these techniques as well as their relationship to other established approaches. Illustration by simple numerical examples takes priority over abstract theory.

Designed for those in statistical process control and total quality management who need a practical guide to the logical basis of data presentation, control charting and capability indexes, this includes topics of cusum methods, and control charts for problem cases.

This textbook and reference book is aimed at statisticians and scientists who would like to gain practical experience with the design and analysis of experiments, with enough theory to understand the analysis of standard and non-standard experimental design.

Providing a self-contained exposition of the theory of linear models, this treatise strikes a compromise between theory and practice, providing a sound theoretical basis while putting the theory to work in important cases.

In recent years, applications of advanced stochastic processes have expanded greatly. Intended for students taking a second course in stochastic processes, this textbook presents an overview of theory with applications in engineering and science. This book covers key topics such as ergodicity, crossing problems, and extremes, and opens the doors to a selection of special topics for the teacher to expand on, like extreme value theory, filter theory, long-range dependence, and point processes, and includes many exercises and examples to illustrate the theory.

Categorical data requires a different methodology and techniques typically not encountered in introductory statistics courses. This title presents various ways of extracting real-life conclusions from contingency tables. It uses a Fisherian approach to categorical data analysis and incorporates numerous examples and real data sets.

With numerous examples using SAS® PROC GLIMMIX, this text presents an introduction to linear modeling using the generalized linear mixed model as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how linear models fit with the rest of the core statistics curriculum and points out the major issues that statistical modelers must consider. Data for all exercises and SAS files for all examples are available at www.crcpress.com.

Providing a much-needed bridge between elementary statistics courses and advanced research methods courses, this text helps students grasp the fundamental assumptions and machinery behind sophisticated statistical topics, such as logistic regression, maximum likelihood, bootstrapping, nonparametrics, and Bayesian methods. The book teaches students how to properly model, think critically, and design their own studies to avoid common errors. It leads them to think differently not only about math and statistics but also about general research and the scientific method.

Suitable for students of survival analysis and reliability, this textbook integrates the use of contemporary statistical software output throughout the text along with a variety of real data sets so readers can see how theories and techniques are applied. It also brings to light linear regression techniques.

Suitable for a one-semester class in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology, this title emphasize parametric models and the advantages of hazard plots over survivor plots. It includes easy-to-follow S programs.

Offers a practical treatment of the techniques and applications of the commonly used procedures for sampling from finite populations. This title includes various topics with illustrations, constructed from practical situations covering a variety of topics, from scholastic aptitude tests to healthcare expenditures and presidential elections.

This book helps readers obtain an in-depth understanding of statistical theory by working on and reviewing solutions to interesting and challenging exercises of practical importance. Unlike similar books, this one incorporates many exercises that apply to real-world settings and provides much more thorough solutions. The exercises and selected detailed solutions cover from basic probability theory through to the theory of statistical inference. By mastering the theoretical statistical strategies necessary to solve the exercises, readers will be prepared to successfully study even higher-level statistical theory.

"Preface This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian methods in applied statistics for general users of and researchers in applied statistics. Although introductory in its early sections, the book is definitely not elementary in the sense of a first text in statistics. The mathematics used in our book is basic probability and statistics, elementary calculus, and linear algebra. A review of probability notation is given in Chapter 1 along with a more detailed list of topics assumed to have been studied. The practical orientation of the book means that the reader's previous experience in probability, statistics, and linear algebra should ideally have included strong computational components. To write an introductory text alone would leave many readers with only a taste of the conceptual elements but no guidance for venturing into genuine practical applications, beyond those where Bayesian methods agree essentially with standard non-Bayesian analyses. On the other hand, we feel it would be a mistake to present the advanced methods without first introducing the basic concepts from our data-analytic perspective. Furthermore, due to the nature of applied statistics, a text on current Bayesian methodology would be incomplete without a variety of worked examples drawn from real applications. To avoid cluttering the main narrative, there are bibliographic notes at the end of each chapter and references at the end of the book"--

Based on the authors¿ lecture notes, this text presents concise yet complete coverage of statistical inference theory, focusing on the fundamental classical principles. Unlike related textbooks, it combines the theoretical basis of statistical inference with a useful applied toolbox that includes linear models. Suitable for a second semester undergraduate course on statistical inference, the text offers proofs to support the mathematics and does not require any use of measure theory. It illustrates core concepts using cartoons and provides solutions to all examples and problems.

Suitable for graduate students and researchers in statistics and biostatistics as well as those in the medical field, epidemiology, and social sciences, this book introduces univariate survival analysis and extends it to the multivariate case. It also covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods.

This text is aimed at both statisticians and researchers in other disciplines such as medicine and psychology who make extensive use of longitudinal data analysis. It is aimed at practical application of the techniques, and an extensive data appendix provides many real data sets.

Drawn from nearly four decades of Lawrence L. Kupper¿s teaching experiences, this text presents theoretical statistical concepts, numerous exercises, and detailed solutions that span topics from basic probability to statistical inference. The text links theoretical principles to real-world situations, including some of the authors¿ own biostatistical work that has addressed complicated design and analysis issues in the health sciences. The authors explain the biostatistical principles needed to solve the exercises, which range in level of difficulty from fairly basic to more challenging. By working through these exercises, students develop a deep understanding of the principles of biostatistical theory.

This is a reissue of the book by statistician R.G. Miller. The text has proved useful in understanding the level of the subject of applied statistics and includes numerous exericises.

Emphasizing the use of WinBUGS and R to analyze real data, thsi book presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data.

Offering insight into the connections between design choice and the resulting statistical analysis, this book explores how experiments are designed using the language of linear statistical models. It also discusses factorial group screening experiments, regression model design, and an introduction to optimal design.

Since the mathematics behind generalized linear models is often difficult to follow while the mathematics behind general linear models is well understood, this text describes the methodology behind both models in a parallel setup.

Bayesian methods have become the widely used statistical methods for data analysis and modeling. The BUGS software has become the popular software for Bayesian analysis worldwide. This title provides a practical introduction to this program and its use. It covers the functionalities of BUGS, including prediction, missing data, and model criticism.

Provides practical guidance on the computer analysis of experimental data. This book connects the objectives of research to the type of experimental design required, describes the actual process of creating the design and collecting the data, shows how to perform the proper analysis of the data, and illustrates the interpretation of results.