Disease mapping involves the analysis of geo-referenced disease incidence data and has many applications, for example within resource allocation, cluster alarm analysis, and ecological studies.Показать полностьюDisease mapping involves the analysis of geo-referenced disease incidence data and has many applications, for example within resource allocation, cluster alarm analysis, and ecological studies. There is a real need amongst public health workers for simpler and more efficient tools for the analysis of geo-referenced disease incidence data. Bayesian and multilevel methods provide the required efficiency, and with the emergence of software packages – such as WinBUGS and MLwiN – are now easy to implement in practice. Provides an introduction to Bayesian and multilevel modelling in disease mapping. Adopts a practical approach, with many detailed worked examples. Includes introductory material on WinBUGS and MLwiN. Discusses three applications in detail – relative risk estimation, focused clustering, and ecological analysis. Suitable for public health workers and epidemiologists with a sound statistical knowledge. Supported by a Website featuring data sets and WinBUGS and MLwiN programs. Disease Mapping with WinBUGS and MLwiN provides a practical introduction to the use of software for disease mapping for researchers, practitioners and graduate students from statistics, public health and epidemiology who analyse disease incidence data.

This book is designed to help students learn how to analyze and interpret research data with intermediate statistics. It is intended to be a supplemental text in an intermediate statistics course in the behavioral sciences or education and it can be used in conjunction with any mainstream text.Показать полностьюThis book is designed to help students learn how to analyze and interpret research data with intermediate statistics. It is intended to be a supplemental text in an intermediate statistics course in the behavioral sciences or education and it can be used in conjunction with any mainstream text. We have found that the book makes SPSS for windows easy to use so that it is not necessary to have a formal, instructional computer lab; you should be able to learn how to use SPSS on your own with this book. Access to the SPSS program and some familiarity with Windows is all that is required. Although SPSS for Windows is quite easy to use, there is such a wide variety of options and statistics that knowing which ones to use and how to interpret the printouts can be difficult, so this book is intended to help with these challenges.

Lemm, a former teacher of physics and psychology at the University of Munster, Germany, applies Bayesian methods to problems in physics, offering practical examples of Bayesian analysis for physicists working in areas such as neural networks, artificial intelligence, and inverse problems in quantum theory.Показать полностьюLemm, a former teacher of physics and psychology at the University of Munster, Germany, applies Bayesian methods to problems in physics, offering practical examples of Bayesian analysis for physicists working in areas such as neural networks, artificial intelligence, and inverse problems in quantum theory. Nonparametric density estimation problems are also discussed, including, as special cases, nonparametric regression and pattern recognition.

Вейвлеты служат важным средством в гармоническом анализе. Ввиду их широкого использования и различности областей применения, даже само слово «вейвлет» разными исследователями понимается по-разному.Показать полностьюВейвлеты служат важным средством в гармоническом анализе. Ввиду их широкого использования и различности областей применения, даже само слово «вейвлет» разными исследователями понимается по-разному. Поэтому, мы должны сразу очертить круг освещаемых вопросов. В настоящем учебнике вейвлеты объясняются на примере анализа экспериментальных временных рядов. Этот процесс выполняется в три этапа: 1) сигнал отображается в пространство вейвлетов (при этом появляется некоторая избыточность). Естественно, что никакой новой информации при этом не появляется, хотя объем цифрового представления увеличивается значительно. 2) в области преобразования выделяются, «высвечиваются» интересующие нас свойства. 3) объем информации уменьшается за счет применения статистических методов. Важно заметить, что эти три шага не являются какими-то фиксированными, жестко определенными. У исследователя существует большая свобода не только в выборе преобразования, но и в количестве используемых шкал анализа, способов выделения интересующих свойств, отсечения незначимой информации. При этом все направлено на то, чтобы получающиеся данные были бы более информативные, чем исходные. Важно только, чтобы применяемые методы не привели бы к потере важных свойств сигнала или к ложному приписыванию ему каких-то черт. При анализе сигналов обычно не ставится задача получения компактного их представления. Уменьшение времени обработки информации является, конечно, важным (особенно при обработке в реальном времени), но оно не должно быть самоцелью и приводить к ухудшения качества анализа.

Applying Generalized Linear Models describes how generalized linear modelling procedures can be used for statistical modelling in many different fields, without becoming lost in problems of statistical inference.Показать полностьюApplying Generalized Linear Models describes how generalized linear modelling procedures can be used for statistical modelling in many different fields, without becoming lost in problems of statistical inference. Many students, even in relatively advanced statistics courses, do not have an overview whereby they can see that the three areas — linear normal, categorical, and survival models — have much in common. The author shows the unity of many of the commonly used models and provides the reader with a taste of many different areas, such as survival models, time series, and spatial analysis. This book should appeal to applied statisticians and to scientists with a basic grounding in modern statistics. With the many exercises included at the ends of chapters, it will be an excellent text for teaching the fundamental uses of statistical modelling. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, and should be familiar at least with the analysis of the simpler normal linear models, regression and ANOVA. The author is professor in the biostatistics department at Limburgs University, Diepenbeek, in the social science department at the University of Liege, and in medical statistics at DeMontfort University, Leicester. He is the author of nine other books.

Separation of signal from noise is the most fundamental problem in data analysis, and arises in many fields, for example, signal processing, econometrics, acturial science, and geostatistics.Показать полностьюSeparation of signal from noise is the most fundamental problem in data analysis, and arises in many fields, for example, signal processing, econometrics, acturial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, and extensions to local likelihood and density estimation. Basic theoretical results and diagnostic tools such as cross validation are introduced along the way. Examples illustrate the implementation of the methods using the LOCFIT software.

Originally developed for evaluation of electronic detection, detection theory was adopted by psychologists as a way to understand sensory decision-making and has been used in areas as diverse as animal behavior and X-ray diagnosis.Показать полностьюOriginally developed for evaluation of electronic detection, detection theory was adopted by psychologists as a way to understand sensory decision-making and has been used in areas as diverse as animal behavior and X-ray diagnosis. This resource for students, behavioral scientists and other researchers explains the basic principles of detection theory. Both one-dimensional and multidimensional models are discussed. The second edition features a new chapter on ideal observers and updated material on adaptive threshold measurement.

Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear.Показать полностьюStatistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications. "Statistical natural-language processing is, in my estimation, one of the most fast-moving and exciting areas of computer science these days. Anyone who wants to learn this field would be well advised to get this book. For that matter, the same goes for anyone who is already in the field. I know that it is going to be one of the most well-thumbed books on my bookshelf."

This graduate textbook introduces the design of experiments with factorial structures, random factor effects, and quantitative predictors and factors, and methods for analyzing the resulting data.Показать полностьюThis graduate textbook introduces the design of experiments with factorial structures, random factor effects, and quantitative predictors and factors, and methods for analyzing the resulting data. The second edition expands coverage of three-level fractional factorial experiments, and the analysis of data from unbalanced experiments, incomplete block designs, split-block designs, and process improvement designs.

Explains computer-based statistical methods for reliability data analysis and test planning for industrial products. Demonstrates how to apply the latest graphical, numerical, and simulation-based methods to a broad range of models found in reliability data analysis, and covers areas such as analyzing degradation data, simulation methods used to complement large-sample asymptotic theory, and data analysis computed with the S-PLUS system.Показать полностьюExplains computer-based statistical methods for reliability data analysis and test planning for industrial products. Demonstrates how to apply the latest graphical, numerical, and simulation-based methods to a broad range of models found in reliability data analysis, and covers areas such as analyzing degradation data, simulation methods used to complement large-sample asymptotic theory, and data analysis computed with the S-PLUS system. Includes chapter exercises using real data sets. For professionals in product reliability and design, and for graduate students in courses in applied reliability data analysis.

This best-selling engineering statistics text provides a practical approach that is more oriented to engineering and the chemical and physical sciences than many similar texts.Показать полностьюThis best-selling engineering statistics text provides a practical approach that is more oriented to engineering and the chemical and physical sciences than many similar texts. It’s packed with unique problem sets that reflect realistic situations engineers will encounter in their working lives. Each copy of the book includes an e-Text on CD — that is a complete electronic version of book. This e-Text features enlarged figures, worked-out solutions, links to data sets for problems solved with a computer, multiple links between glossary terms and text sections for quick and easy reference, and a wealth of additional material to create a dynamic study environment for students. Suitable for a one- or two-term Jr/Sr course in probability and statistics for all engineering majors.

Focusing on real data, critical thinking, and contemporary technology, this textbook offers a data analysis approach to the subject while assuming only a minimum of mathematical knowledge. An accompanying CD includes worked examples and practice exercises, illustrations and video clips, and a graphing program. Moore teaches at Purdue University.

Adopting an intuitive, informal style, this text emphasizes the statistical concepts and assumptions needed to describe and make inferences about real data.Показать полностьюAdopting an intuitive, informal style, this text emphasizes the statistical concepts and assumptions needed to describe and make inferences about real data. The volume’s 21 chapters address topics including univariate distributions; the chi-square and F distributions; contrasts among means; trend analysis; repeated- measures designs; hierarchical designs; Latin squares and related designs; correlation and bivariate regression; and multiple regression. Includes major content and organizational revisions from the previous edition. Suitable for graduate and advanced undergraduate students learning about data analysis, as well as for researchers.

Univ. College London, UK. Basic introduction to the underlying concepts of medical statistics and guide to commonly used procedures. Key features include: worked examples to accompany each topic, and emphasis on computer analysis of data rather than hand calculations. To be used as a reference and revision summary.

This book is classic work on statistical inference remains highly applicable to statistical analysis. The purpose of this book is to present the theory and techniques of statistical inference in a logically integrated and practical form.

Provides a current account of the theory and applications of linear models. Presents a unified theory of inference from linear models with minimal assumptions through least squares theory, and using alternative methods of estimation and testing based on convex loss functions and general estimating equations.

Textbook designed as a supplement to an understanding of statistical concepts for the purpose of employing sophisticated and powerful statistical methods. Emphasizes the drawing of scientific inferences from the regression analysis of data examples.

Researchers in multilevel modeling (MLM) report on new statistical advances, methodological issues, and applications in MLM, and examine problems that occur when trying to use MLM in applied research in areas such as power, experimental design, and model violations.Показать полностьюResearchers in multilevel modeling (MLM) report on new statistical advances, methodological issues, and applications in MLM, and examine problems that occur when trying to use MLM in applied research in areas such as power, experimental design, and model violations. The book will be of interest to researchers in advanced statistical training and experience in applying MLMs, especially in the areas of education, clinical intervention, psychology, and other behavioral sciences. The book can also be used as a supplement for an introductory graduate course. Reise teaches measurement at the University of California-Los Angeles. Duan teaches psychiatry and biostatistics at the University of California-Los Angeles.

This textbook extends univariate procedures with one dependent variable to analogous multivariate techniques involving several dependent variables, and finds functions of variables that discriminate among groups in the data and that reveal the basic dimensionality and characteristic patterns of the data. The second edition adds two chapters on cluster analysis and graphical techniques.

Rosenberger (mathematics and statistics, University of Maryland) and Lachin (biostatistics and statistics, George Washington University) combine applied aspects of randomization in clinical trials with a probabilistic treatment of properties of randomization in this text/reference for biostatistics graduate students and biostatisticians in practice.Показать полностьюRosenberger (mathematics and statistics, University of Maryland) and Lachin (biostatistics and statistics, George Washington University) combine applied aspects of randomization in clinical trials with a probabilistic treatment of properties of randomization in this text/reference for biostatistics graduate students and biostatisticians in practice. Taking a non-Bayesian and nonparametric approach to inference, they focus on the linear rank test under a randomization model, and discuss likelihood-based inference as well.