Introduction to Time Series Analysis and Forecasting

Author: Douglas C. Montgomery,Cheryl L. Jennings,Murat Kulahci

Publisher: John Wiley & Sons

ISBN: 1118745159

Category: Mathematics

Page: 672

View: 5265

DOWNLOAD NOW »

Praise for the First Edition "…[t]he book is great for readers who need to applythe methods and models presented but have little background inmathematics and statistics." -MAA Reviews Thoroughly updated throughout, Introduction to Time SeriesAnalysis and Forecasting, Second Edition presents theunderlying theories of time series analysis that are needed toanalyze time-oriented data and construct real-world short- tomedium-term statistical forecasts. Authored by highly-experienced academics and professionals inengineering statistics, the Second Edition featuresdiscussions on both popular and modern time series methodologies aswell as an introduction to Bayesian methods in forecasting.Introduction to Time Series Analysis and Forecasting, SecondEdition also includes: Over 300 exercises from diverse disciplines including healthcare, environmental studies, engineering, and finance More than 50 programming algorithms using JMP®, SAS®,and R that illustrate the theory and practicality of forecastingtechniques in the context of time-oriented data New material on frequency domain and spatial temporaldata analysis Expanded coverage of the variogram and spectrum withapplications as well as transfer and intervention modelfunctions A supplementary website featuring PowerPoint®slides, data sets, and select solutions to the problems Introduction to Time Series Analysis and Forecasting, SecondEdition is an ideal textbook upper-undergraduate andgraduate-levels courses in forecasting and time series. The book isalso an excellent reference for practitioners and researchers whoneed to model and analyze time series data to generate forecasts.

Time Series Analysis and Forecasting by Example

Author: Søren Bisgaard,Murat Kulahci

Publisher: John Wiley & Sons

ISBN: 9781118056950

Category: Mathematics

Page: 400

View: 7966

DOWNLOAD NOW »

An intuition-based approach enables you to master time series analysis with ease Time Series Analysis and Forecasting by Example provides the fundamental techniques in time series analysis using various examples. By introducing necessary theory through examples that showcase the discussed topics, the authors successfully help readers develop an intuitive understanding of seemingly complicated time series models and their implications. The book presents methodologies for time series analysis in a simplified, example-based approach. Using graphics, the authors discuss each presented example in detail and explain the relevant theory while also focusing on the interpretation of results in data analysis. Following a discussion of why autocorrelation is often observed when data is collected in time, subsequent chapters explore related topics, including: Graphical tools in time series analysis Procedures for developing stationary, non-stationary, and seasonal models How to choose the best time series model Constant term and cancellation of terms in ARIMA models Forecasting using transfer function-noise models The final chapter is dedicated to key topics such as spurious relationships, autocorrelation in regression, and multiple time series. Throughout the book, real-world examples illustrate step-by-step procedures and instructions using statistical software packages such as SAS®, JMP, Minitab, SCA, and R. A related Web site features PowerPoint slides to accompany each chapter as well as the book's data sets. With its extensive use of graphics and examples to explain key concepts, Time Series Analysis and Forecasting by Example is an excellent book for courses on time series analysis at the upper-undergraduate and graduate levels. it also serves as a valuable resource for practitioners and researchers who carry out data and time series analysis in the fields of engineering, business, and economics.

Introduction to Time Series Analysis and Forecasting, Solutions Manual

Author: Douglas C. Montgomery,Cheryl L. Jennings,Murat Kulahci

Publisher: Wiley

ISBN: 9780470435748

Category: Mathematics

Page: 88

View: 2744

DOWNLOAD NOW »

An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data. Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts. Seven easy-to-follow chapters provide intuitive explanations and in-depth coverage of key forecasting topics, including: Regression-based methods, heuristic smoothing methods, and general time series models Basic statistical tools used in analyzing time series data Metrics for evaluating forecast errors and methods for evaluating and tracking forecasting performance over time Cross-section and time series regression data, least squares and maximum likelihood model fitting, model adequacy checking, prediction intervals, and weighted and generalized least squares Exponential smoothing techniques for time series with polynomial components and seasonal data Forecasting and prediction interval construction with a discussion on transfer function models as well as intervention modeling and analysis Multivariate time series problems, ARCH and GARCH models, and combinations of forecasts The ARIMA model approach with a discussion on how to identify and fit these models for non-seasonal and seasonal time series The intricate role of computer software in successful time series analysis is acknowledged with the use of Minitab, JMP, and SAS software applications, which illustrate how the methods are imple-mented in practice. An extensive FTP site is available for readers to obtain data sets, Microsoft Office PowerPoint slides, and selected answers to problems in the book. Requiring only a basic working knowledge of statistics and complete with exercises at the end of each chapter as well as examples from a wide array of fields, Introduction to Time Series Analysis and Forecasting is an ideal text for forecasting and time series courses at the advanced undergraduate and beginning graduate levels. The book also serves as an indispensable reference for practitioners in business, economics, engineering, statistics, mathematics, and the social, environmental, and life sciences.

Introduction to Time Series Analysis and Forecasting Solutions Set

Author: Douglas C. Montgomery,Cheryl L. Jennings,Murat Kulahci

Publisher: Wiley

ISBN: 9780470501474

Category: Mathematics

Page: 547

View: 4375

DOWNLOAD NOW »

This set contains Introduction to Time Series Analysis and Forecasting text ISBN 978-0-471-65397-4 and Introduction to Time Series Analysis and Forecasting, Solutions Manual ISBN 978-0-470-43574-8.

Zeitreihenmodelle

Author: Andrew C. Harvey

Publisher: De Gruyter Oldenbourg

ISBN: 9783486230062

Category:

Page: 379

View: 5503

DOWNLOAD NOW »

Gegenstand des Werkes sind Analyse und Modellierung von Zeitreihen. Es wendet sich an Studierende und Praktiker aller Disziplinen, in denen Zeitreihenbeobachtungen wichtig sind.

Multivariate Time Series Analysis

With R and Financial Applications

Author: Ruey S. Tsay

Publisher: John Wiley & Sons

ISBN: 1118617754

Category: Mathematics

Page: 520

View: 9645

DOWNLOAD NOW »

An accessible guide to the multivariate time series toolsused in numerous real-world applications Multivariate Time Series Analysis: With R and FinancialApplications is the much anticipated sequel coming from one ofthe most influential and prominent experts on the topic of timeseries. Through a fundamental balance of theory and methodology,the book supplies readers with a comprehensible approach tofinancial econometric models and their applications to real-worldempirical research. Differing from the traditional approach to multivariate timeseries, the book focuses on reader comprehension by emphasizingstructural specification, which results in simplified parsimoniousVAR MA modeling. Multivariate Time Series Analysis: With R andFinancial Applications utilizes the freely available Rsoftware package to explore complex data and illustrate relatedcomputation and analyses. Featuring the techniques and methodologyof multivariate linear time series, stationary VAR models, VAR MAtime series and models, unitroot process, factor models, andfactor-augmented VAR models, the book includes: • Over 300 examples and exercises to reinforce thepresented content • User-friendly R subroutines and research presentedthroughout to demonstrate modern applications • Numerous datasets and subroutines to provide readerswith a deeper understanding of the material Multivariate Time Series Analysis is an ideal textbookfor graduate-level courses on time series and quantitative financeand upper-undergraduate level statistics courses in time series.The book is also an indispensable reference for researchers andpractitioners in business, finance, and econometrics.

Time Series Analysis

Author: Wilfredo Palma

Publisher: John Wiley & Sons

ISBN: 1118634322

Category: Mathematics

Page: 616

View: 7195

DOWNLOAD NOW »

A modern and accessible guide to the analysis of introductory time series data Featuring an organized and self-contained guide, Time Series Analysis provides a broad introduction to the most fundamental methodologies and techniques of time series analysis. The book focuses on the treatment of univariate time series by illustrating a number of well-known models such as ARMA and ARIMA. Providing contemporary coverage, the book features several useful and newly-developed techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, local stationarity, missing values and outliers, and threshold models. Time Series Analysis includes practical applications of time series methods throughout, as well as: Real-world examples and exercise sets that allow readers to practice the presented methods and techniques Numerous detailed analyses of computational aspects related to the implementation of methodologies including algorithm efficiency, arithmetic complexity, and process time End-of-chapter proposed problems and bibliographical notes to deepen readers’ knowledge of the presented material Appendices that contain details on fundamental concepts and select solutions of the problems implemented throughout A companion website with additional data files and computer codes Time Series Analysis is an excellent textbook for undergraduate and beginning graduate-level courses in time series as well as a supplement for students in advanced statistics, mathematics, economics, finance, engineering, and physics. The book is also a useful reference for researchers and practitioners in time series analysis, econometrics, and finance. Wilfredo Palma, PhD, is Professor of Statistics in the Department of Statistics at Pontificia Universidad Católica de Chile. Dr. Palma has published several refereed articles and has received over a dozen academic honors and awards. His research interests include time series analysis, prediction theory, state space systems, linear models, and econometrics. He is the author of Long-Memory Time Series: Theory and Methods, also published by Wiley.

Statistical Methods for Forecasting

Author: Bovas Abraham,Johannes Ledolter

Publisher: John Wiley & Sons

ISBN: 0470317299

Category: Mathematics

Page: 445

View: 2111

DOWNLOAD NOW »

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "This book, it must be said, lives up to the words on its advertising cover: 'Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical, intermediate level discussion of a variety of forecasting tools, and explains how they relate to one another, both in theory and practice.' It does just that!" -Journal of the Royal Statistical Society "A well-written work that deals with statistical methods and models that can be used to produce short-term forecasts, this book has wide-ranging applications. It could be used in the context of a study of regression, forecasting, and time series analysis by PhD students; or to support a concentration in quantitative methods for MBA students; or as a work in applied statistics for advanced undergraduates." -Choice Statistical Methods for Forecasting is a comprehensive, readable treatment of statistical methods and models used to produce short-term forecasts. The interconnections between the forecasting models and methods are thoroughly explained, and the gap between theory and practice is successfully bridged. Special topics are discussed, such as transfer function modeling; Kalman filtering; state space models; Bayesian forecasting; and methods for forecast evaluation, comparison, and control. The book provides time series, autocorrelation, and partial autocorrelation plots, as well as examples and exercises using real data. Statistical Methods for Forecasting serves as an outstanding textbook for advanced undergraduate and graduate courses in statistics, business, engineering, and the social sciences, as well as a working reference for professionals in business, industry, and government.

Analysis of Financial Time Series

Author: Ruey S. Tsay

Publisher: John Wiley & Sons

ISBN: 9781118017098

Category: Mathematics

Page: 720

View: 7994

DOWNLOAD NOW »

This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described. The author begins with basic characteristics of financial time series data before covering three main topics: Analysis and application of univariate financial time series The return series of multiple assets Bayesian inference in finance methods Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets. The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.

Introduction to Linear Regression Analysis

Author: Douglas C. Montgomery,Elizabeth A. Peck,G. Geoffrey Vining

Publisher: John Wiley & Sons

ISBN: 1119180171

Category: Mathematics

Page: 672

View: 5347

DOWNLOAD NOW »

Praise for the Fourth Edition "As with previous editions, the authors have produced a leadingtextbook on regression." —Journal of the American Statistical Association A comprehensive and up-to-date introduction to thefundamentals of regression analysis Introduction to Linear Regression Analysis, Fifth Editioncontinues to present both the conventional and less common uses oflinear regression in today’s cutting-edge scientificresearch. The authors blend both theory and application to equipreaders with an understanding of the basic principles needed toapply regression model-building techniques in various fields ofstudy, including engineering, management, and the healthsciences. Following a general introduction to regression modeling,including typical applications, a host of technical tools areoutlined such as basic inference procedures, introductory aspectsof model adequacy checking, and polynomial regression models andtheir variations. The book then discusses how transformations andweighted least squares can be used to resolve problems of modelinadequacy and also how to deal with influential observations. TheFifth Edition features numerous newly added topics,including: A chapter on regression analysis of time series data thatpresents the Durbin-Watson test and other techniques for detectingautocorrelation as well as parameter estimation in time seriesregression models Regression models with random effects in addition to adiscussion on subsampling and the importance of the mixedmodel Tests on individual regression coefficients and subsets ofcoefficients Examples of current uses of simple linear regression models andthe use of multiple regression models for understanding patientsatisfaction data. In addition to Minitab, SAS, and S-PLUS, the authors haveincorporated JMP and the freely available R software to illustratethe discussed techniques and procedures in this new edition.Numerous exercises have been added throughout, allowing readers totest their understanding of the material. Introduction to Linear Regression Analysis, Fifth Editionis an excellent book for statistics and engineering courses onregression at the upper-undergraduate and graduate levels. The bookalso serves as a valuable, robust resource for professionals in thefields of engineering, life and biological sciences, and the socialsciences.

An Introduction to Analysis of Financial Data with R

Author: Ruey S. Tsay

Publisher: John Wiley & Sons

ISBN: 1119013461

Category: Business & Economics

Page: 416

View: 6988

DOWNLOAD NOW »

A complete set of statistical tools for beginning financial analysts from a leading authority Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research. The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including: Linear time series analysis, with coverage of exponential smoothing for forecasting and methods for model comparison Different approaches to calculating asset volatility and various volatility models High-frequency financial data and simple models for price changes, trading intensity, and realized volatility Quantitative methods for risk management, including value at risk and conditional value at risk Econometric and statistical methods for risk assessment based on extreme value theory and quantile regression Throughout the book, the visual nature of the topic is showcased through graphical representations in R, and two detailed case studies demonstrate the relevance of statistics in finance. A related website features additional data sets and R scripts so readers can create their own simulations and test their comprehension of the presented techniques. An Introduction to Analysis of Financial Data with R is an excellent book for introductory courses on time series and business statistics at the upper-undergraduate and graduate level. The book is also an excellent resource for researchers and practitioners in the fields of business, finance, and economics who would like to enhance their understanding of financial data and today's financial markets.

Introduction to Time-series Modeling and Forecasting in Business and Economics

Author: Patricia E. Gaynor,Rickey C. Kirkpatrick

Publisher: McGraw-Hill College

ISBN: 9780070349131

Category: Business & Economics

Page: 625

View: 6266

DOWNLOAD NOW »

This text is designed for forecasting courses in economics, management science and decision science departments, and emphasizing understanding and application rather than the theoretical and computational aspects of the statistical techniques used. Chapter 2 on describing and transforming data and chapter 9 on single equation econometric modelling offer in-depth discussions of topics fundamental to time-series analysis that most other texts cover in a cursory fashion.

An Introduction to Discrete-Valued Time Series

Author: Christian H. Weiss

Publisher: John Wiley & Sons

ISBN: 1119096960

Category: Mathematics

Page: 304

View: 4449

DOWNLOAD NOW »

A first approach for modeling time series of counts : the thinning-based INAR (1) model -- Further thinning-based models for count time series -- INGARCH models for count time series -- Further models for count time series -- Analyzing categorical time series -- Models for categorical time series -- Control charts for count processes -- Control charts for categorical processes

Wahrscheinlichkeitsrechnung und Statistik

Author: Robert Hafner

Publisher: Springer-Verlag

ISBN: 3709169445

Category: Mathematics

Page: 512

View: 5972

DOWNLOAD NOW »

Das Buch ist eine Einführung in die Wahrscheinlichkeitsrechnung und mathematische Statistik auf mittlerem mathematischen Niveau. Die Pädagogik der Darstellung unterscheidet sich in wesentlichen Teilen – Einführung der Modelle für unabhängige und abhängige Experimente, Darstellung des Suffizienzbegriffes, Ausführung des Zusammenhanges zwischen Testtheorie und Theorie der Bereichschätzung, allgemeine Diskussion der Modellentwicklung – erheblich von der anderer vergleichbarer Lehrbücher. Die Darstellung ist, soweit auf diesem Niveau möglich, mathematisch exakt, verzichtet aber bewußt und ebenfalls im Gegensatz zu vergleichbaren Texten auf die Erörterung von Meßbarkeitsfragen. Der Leser wird dadurch erheblich entlastet, ohne daß wesentliche Substanz verlorengeht. Das Buch will allen, die an der Anwendung der Statistik auf solider Grundlage interessiert sind, eine Einführung bieten, und richtet sich an Studierende und Dozenten aller Studienrichtungen, für die mathematische Statistik ein Werkzeug ist.

Time Series

Applications to Finance with R and S-Plus

Author: Ngai Hang Chan

Publisher: John Wiley & Sons

ISBN: 1118030710

Category: Mathematics

Page: 330

View: 7136

DOWNLOAD NOW »

A new edition of the comprehensive, hands-on guide to financialtime series, now featuring S-Plus® and R software Time Series: Applications to Finance with R and S-Plus®,Second Edition is designed to present an in-depth introduction tothe conceptual underpinnings and modern ideas of time seriesanalysis. Utilizing interesting, real-world applications and thelatest software packages, this book successfully helps readersgrasp the technical and conceptual manner of the topic in order togain a deeper understanding of the ever-changing dynamics of thefinancial world. With balanced coverage of both theory and applications, thisSecond Edition includes new content to accurately reflect thecurrent state-of-the-art nature of financial time series analysis.A new chapter on Markov Chain Monte Carlo presents Bayesian methodsfor time series with coverage of Metropolis-Hastings algorithm,Gibbs sampling, and a case study that explores the relevance ofthese techniques for understanding activity in the Dow JonesIndustrial Average. The author also supplies a new presentation ofstatistical arbitrage that includes discussion of pairs trading andcointegration. In addition to standard topics such as forecastingand spectral analysis, real-world financial examples are used toillustrate recent developments in nonstandard techniques,including: Nonstationarity Heteroscedasticity Multivariate time series State space modeling and stochastic volatility Multivariate GARCH Cointegration and common trends The book's succinct and focused organization allows readers tograsp the important ideas of time series. All examples aresystematically illustrated with S-Plus® and R software,highlighting the relevance of time series in financialapplications. End-of-chapter exercises and selected solutions allowreaders to test their comprehension of the presented material, anda related Web site features additional data sets. Time Series: Applications to Finance with R and S-Plus® isan excellent book for courses on financial time series at theupper-undergraduate and beginning graduate levels. It also servesas an indispensible resource for practitioners working withfinancial data in the fields of statistics, economics, business,and risk management.

Nonparametric Statistics with Applications to Science and Engineering

Author: Paul H. Kvam,Brani Vidakovic

Publisher: John Wiley & Sons

ISBN: 9780470168691

Category: Mathematics

Page: 448

View: 7084

DOWNLOAD NOW »

A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing. Nonparametric Statistics with Applications to Science and Engineering begins with succinct coverage of basic results for order statistics, methods of categorical data analysis, nonparametric regression, and curve fitting methods. The authors then focus on nonparametric procedures that are becoming more relevant to engineering researchers and practitioners. The important fundamental materials needed to effectively learn and apply the discussed methods are also provided throughout the book. Complete with exercise sets, chapter reviews, and a related Web site that features downloadable MATLAB applications, this book is an essential textbook for graduate courses in engineering and the physical sciences and also serves as a valuable reference for researchers who seek a more comprehensive understanding of modern nonparametric statistical methods.

Bayesian Statistical Modelling

Author: Peter Congdon

Publisher: John Wiley & Sons

ISBN: 0470035935

Category: Mathematics

Page: 596

View: 3823

DOWNLOAD NOW »

Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets. The second edition: Provides an integrated presentation of theory, examples, applications and computer algorithms. Discusses the role of Markov Chain Monte Carlo methods in computing and estimation. Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences. Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles. Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs. Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. Praise for the First Edition: “It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains.” – ISI - Short Book Reviews “This is an excellent introductory book on Bayesian modelling techniques and data analysis” – Biometrics “The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods.” – Journal of Mathematical Psychology

The EM Algorithm and Extensions

Author: Geoffrey McLachlan,Thriyambakam Krishnan

Publisher: John Wiley & Sons

ISBN: 0470191600

Category: Mathematics

Page: 384

View: 3053

DOWNLOAD NOW »

The only single-source——now completely updated and revised——to offer a unified treatment of the theory, methodology, and applications of the EM algorithm Complete with updates that capture developments from the past decade, The EM Algorithm and Extensions, Second Edition successfully provides a basic understanding of the EM algorithm by describing its inception, implementation, and applicability in numerous statistical contexts. In conjunction with the fundamentals of the topic, the authors discuss convergence issues and computation of standard errors, and, in addition, unveil many parallels and connections between the EM algorithm and Markov chain Monte Carlo algorithms. Thorough discussions on the complexities and drawbacks that arise from the basic EM algorithm, such as slow convergence and lack of an in-built procedure to compute the covariance matrix of parameter estimates, are also presented. While the general philosophy of the First Edition has been maintained, this timely new edition has been updated, revised, and expanded to include: New chapters on Monte Carlo versions of the EM algorithm and generalizations of the EM algorithm New results on convergence, including convergence of the EM algorithm in constrained parameter spaces Expanded discussion of standard error computation methods, such as methods for categorical data and methods based on numerical differentiation Coverage of the interval EM, which locates all stationary points in a designated region of the parameter space Exploration of the EM algorithm's relationship with the Gibbs sampler and other Markov chain Monte Carlo methods Plentiful pedagogical elements—chapter introductions, lists of examples, author and subject indices, computer-drawn graphics, and a related Web site The EM Algorithm and Extensions, Second Edition serves as an excellent text for graduate-level statistics students and is also a comprehensive resource for theoreticians, practitioners, and researchers in the social and physical sciences who would like to extend their knowledge of the EM algorithm.

Recent Advances in Quantitative Methods in Cancer and Human Health Risk Assessment

Author: Lutz Edler,Christos Kitsos

Publisher: John Wiley & Sons

ISBN: 0470857668

Category: Mathematics

Page: 502

View: 7729

DOWNLOAD NOW »

Human health risk assessment involves the measuring of risk of exposure to disease, with a view to improving disease prevention. Mathematical, biological, statistical, and computational methods play a key role in exposure assessment, hazard assessment and identification, and dose-response modelling. Recent Advances in Quantitative Methods in Cancer and Human Health Risk Assessment is a comprehensive text that accounts for the wealth of new biological data as well as new biological, toxicological, and medical approaches adopted in risk assessment. It provides an authoritative compendium of state-of-the-art methods proposed and used, featuring contributions from eminent authors with varied experience from academia, government, and industry. Provides a comprehensive summary of currently available quantitative methods for risk assessment of both cancer and non-cancer problems. Describes the applications and the limitations of current mathematical modelling and statistical analysis methods (classical and Bayesian). Includes an extensive introduction and discussion to each chapter. Features detailed studies of risk assessments using biologically-based modelling approaches. Discusses the varying computational aspects of the methods proposed. Provides a global perspective on human health risk assessment by featuring case studies from a wide range of countries. Features an extensive bibliography with links to relevant background information within each chapter. Recent Advances in Quantitative Methods in Cancer and Human Health Risk Assessment will appeal to researchers and practitioners in public health & epidemiology, and postgraduate students alike. It will also be of interest to professionals working in risk assessment agencies.