Statistical Methods for Rates and Proportions

Author: Joseph L. Fleiss,Bruce Levin,Myunghee Cho Paik

Publisher: John Wiley & Sons

ISBN: 1118625617

Category: Medical

Page: 800

View: 8272

"This book is to be recommended as a standard shelf reference . . . and as a ‘must’ to be read by all who wish to better use and understand data involving dichotomous or dichotomizable measurements." —American Journal of Psychiatry In the two decades since the second edition of Statistical Methods for Rates and Proportions was published, evolving technologies and new methodologies have significantly changed the way today’s statistics are viewed and handled. The explosive development of personal computing and statistical software has facilitated the sophisticated analysis of data, putting capabilities that were once the domain of specialists into the hands of every researcher. The Third Edition of this important text addresses these changes and brings the literature up to date. While the previous edition focused on the use of desktop and handheld calculators, the new edition takes full advantage of modern computing power without losing the elegant simplicity that made the text so popular with students and practitioners alike. In authoritative yet clear terminology, the authors have brought the science of data analysis up to date without compromising its accessibility. Features of the Third Edition include: New material on sample size calculations and issues in clinical trials, and entirely new chapters on single-sample data, logistic regression, Poisson regression, regression models for matched samples, the analysis of correlated binary data, and methods for analyzing fourfold tables with missing data The addition of many new problems, both numerical and theoretical Answer sections for numerical problems and hints for tackling the theoretical ones A frequentist approach enhanced by the inclusion of empirical Bayesian methodology where appropriate Combining the latest research with the original studies that established the previous editions as leaders in the field, Statistical Methods for Rates and Proportions, Third Edition will continue to be an invaluable resource for students, statisticians, biostatisticians, and epidemiologists.

Statistical Methods for Rates and Proportions

Author: Joseph L. Fleiss,Bruce Levin,Myunghee Cho Paik

Publisher: John Wiley and Sons

ISBN: 0471458619

Category: Medical

Page: 800

View: 2328

"This book is to be recommended as a standard shelf reference . . .and as a ‘must’ to be read by all who wish to betteruse and understand data involving dichotomous or dichotomizablemeasurements." —American Journal of Psychiatry In the two decades since the second edition of StatisticalMethods for Rates and Proportions was published, evolvingtechnologies and new methodologies have significantly changed theway today’s statistics are viewed and handled. The explosivedevelopment of personal computing and statistical software hasfacilitated the sophisticated analysis of data, puttingcapabilities that were once the domain of specialists into thehands of every researcher. The Third Edition of this important text addresses thesechanges and brings the literature up to date. While the previousedition focused on the use of desktop and handheld calculators, thenew edition takes full advantage of modern computing power withoutlosing the elegant simplicity that made the text so popular withstudents and practitioners alike. In authoritative yet clearterminology, the authors have brought the science of data analysisup to date without compromising its accessibility. Features of the Third Edition include: New material on sample size calculations and issues in clinicaltrials, and entirely new chapters on single-sample data, logisticregression, Poisson regression, regression models for matchedsamples, the analysis of correlated binary data, and methods foranalyzing fourfold tables with missing data The addition of many new problems, both numerical andtheoretical Answer sections for numerical problems and hints for tacklingthe theoretical ones A frequentist approach enhanced by the inclusion of empiricalBayesian methodology where appropriate Combining the latest research with the original studies thatestablished the previous editions as leaders in the field,Statistical Methods for Rates and Proportions, Third Editionwill continue to be an invaluable resource for students,statisticians, biostatisticians, and epidemiologists.

Advances in Statistical Methods for the Health Sciences

Applications to Cancer and AIDS Studies, Genome Sequence Analysis, and Survival Analysis

Author: Jean-Louis Auget,N. Balakrishnan,Mounir Mesbah,Geert Molenberghs

Publisher: Springer Science & Business Media

ISBN: 9780817645427

Category: Mathematics

Page: 540

View: 8880

Statistical methods have become an increasingly important and integral part of research in the health sciences. Many sophisticated methodologies have been developed for specific applications and problems. This self-contained comprehensive volume covers a wide range of topics pertaining to new statistical methods in the health sciences, including epidemiology, pharmacovigilance, quality of life, survival analysis, and genomics. The book will serve the health science community as well as practitioners, researchers, and graduate students in applied probability, statistics, and biostatistics.

Statistical Methods for Survival Data Analysis

Author: Elisa T. Lee,John Wenyu Wang

Publisher: John Wiley & Sons

ISBN: 1118593057

Category: Mathematics

Page: 512

View: 9773

Praise for the Third Edition “. . . an easy-to read introduction to survival analysiswhich covers the major concepts and techniques of thesubject.” —Statistics in Medical Research Updated and expanded to reflect the latest developments,Statistical Methods for Survival Data Analysis, FourthEdition continues to deliver a comprehensive introduction tothe most commonly-used methods for analyzing survival data.Authored by a uniquely well-qualified author team, the FourthEdition is a critically acclaimed guide to statistical methods withapplications in clinical trials, epidemiology, areas of business,and the social sciences. The book features many real-world examplesto illustrate applications within these various fields, althoughspecial consideration is given to the study of survival data inbiomedical sciences. Emphasizing the latest research and providing the mostup-to-date information regarding software applications in thefield, Statistical Methods for Survival Data Analysis, FourthEdition also includes: Marginal and random effect models for analyzing correlatedcensored or uncensored data Multiple types of two-sample and K-sample comparisonanalysis Updated treatment of parametric methods for regression modelfitting with a new focus on accelerated failure time models Expanded coverage of the Cox proportional hazards model Exercises at the end of each chapter to deepen knowledge of thepresented material Statistical Methods for Survival Data Analysis is anideal text for upper-undergraduate and graduate-level courses onsurvival data analysis. The book is also an excellent resource forbiomedical investigators, statisticians, and epidemiologists, aswell as researchers in every field in which the analysis ofsurvival data plays a role.

Health Measurement Scales

A practical guide to their development and use

Author: David L. Streiner,Geoffrey R. Norman,John Cairney

Publisher: OUP Oxford

ISBN: 0191508330

Category: Medical

Page: 448

View: 3276

Clinicians and those in health sciences are frequently called upon to measure subjective states such as attitudes, feelings, quality of life, educational achievement and aptitude, and learning style in their patients. This fifth edition of Health Measurement Scales enables these groups to both develop scales to measure non-tangible health outcomes, and better evaluate and differentiate between existing tools. Health Measurement Scales is the ultimate guide to developing and validating measurement scales that are to be used in the health sciences. The book covers how the individual items are developed; various biases that can affect responses (e.g. social desirability, yea-saying, framing); various response options; how to select the best items in the set; how to combine them into a scale; and finally how to determine the reliability and validity of the scale. It concludes with a discussion of ethical issues that may be encountered, and guidelines for reporting the results of the scale development process. Appendices include a comprehensive guide to finding existing scales, and a brief introduction to exploratory and confirmatory factor analysis, making this book a must-read for any practitioner dealing with this kind of data.

Nonparametric Statistical Methods

Author: Myles Hollander,Douglas A. Wolfe,Eric Chicken

Publisher: John Wiley & Sons

ISBN: 1118553292

Category: Mathematics

Page: 848

View: 5532

Praise for the Second Edition “This book should be an essential part of the personallibrary of every practicingstatistician.”—Technometrics Thoroughly revised and updated, the new edition of NonparametricStatistical Methods includes additional modern topics andprocedures, more practical data sets, and new problems fromreal-life situations. The book continues to emphasize theimportance of nonparametric methods as a significant branch ofmodern statistics and equips readers with the conceptual andtechnical skills necessary to select and apply the appropriateprocedures for any given situation. Written by leading statisticians, Nonparametric StatisticalMethods, Third Edition provides readers with crucialnonparametric techniques in a variety of settings, emphasizing theassumptions underlying the methods. The book provides an extensivearray of examples that clearly illustrate how to use nonparametricapproaches for handling one- or two-sample location and dispersionproblems, dichotomous data, and one-way and two-way layoutproblems. In addition, the Third Edition features: The use of the freely available R software to aid incomputation and simulation, including many new R programs writtenexplicitly for this new edition New chapters that address density estimation, wavelets,smoothing, ranked set sampling, and Bayesian nonparametrics Problems that illustrate examples from agricultural science,astronomy, biology, criminology, education, engineering,environmental science, geology, home economics, medicine,oceanography, physics, psychology, sociology, and spacescience Nonparametric Statistical Methods, Third Edition is anexcellent reference for applied statisticians and practitioners whoseek a review of nonparametric methods and their relevantapplications. The book is also an ideal textbook forupper-undergraduate and first-year graduate courses in appliednonparametric statistics.

Statistical Modeling by Wavelets

Author: Brani Vidakovic

Publisher: John Wiley & Sons

ISBN: 0470317868

Category: Mathematics

Page: 408

View: 9906

A comprehensive, step-by-step introduction to wavelets in statistics. What are wavelets? What makes them increasingly indispensable in statistical nonparametrics? Why are they suitable for "time-scale" applications? How are they used to solve such problems as denoising, regression, or density estimation? Where can one find up-to-date information on these newly "discovered" mathematical objects? These are some of the questions Brani Vidakovic answers in Statistical Modeling by Wavelets. Providing a much-needed introduction to the latest tools afforded statisticians by wavelet theory, Vidakovic compiles, organizes, and explains in depth research data previously available only in disparate journal articles. He carefully balances both statistical and mathematical techniques, supplementing the material with a wealth of examples, more than 100 illustrations, and extensive references-with data sets and S-Plus wavelet overviews made available for downloading over the Internet. Both introductory and data-oriented modeling topics are featured, including: * Continuous and discrete wavelet transformations. * Statistical optimality properties of wavelet shrinkage. * Theoretical aspects of wavelet density estimation. * Bayesian modeling in the wavelet domain. * Properties of wavelet-based random functions and densities. * Several novel and important wavelet applications in statistics. * Wavelet methods in time series. Accessible to anyone with a background in advanced calculus and algebra, Statistical Modeling by Wavelets promises to become the standard reference for statisticians and engineers seeking a comprehensive introduction to an emerging field.

Linear Statistical Models

Author: James H. Stapleton

Publisher: John Wiley & Sons

ISBN: 0470317760

Category: Mathematics

Page: 472

View: 3100

Linear Statistical Models Developed and refined over a period of twenty years, the material in this book offers an especially lucid presentation of linear statistical models. These models lead to what is usually called "multiple regression" or "analysis of variance" methodology, which, in turn, opens up a wide range of applications to the physical, biological, and social sciences, as well as to business, agriculture, and engineering. Unlike similar books on this topic, Linear Statistical Models emphasizes the geometry of vector spaces because of the intuitive insights this approach brings to an understanding of the theory. While the focus is on theory, examples of applications, using the SAS and S-Plus packages, are included. Prerequisites include some familiarity with linear algebra, and probability and statistics at the postcalculus level. Major topics covered include: * Methods of study of random vectors, including the multivariate normal, chi-square, t and F distributions, central and noncentral * The linear model and the basic theory of regression analysis and the analysis of variance * Multiple regression methods, including transformations, analysis of residuals, and asymptotic theory for regression analysis. Separate sections are devoted to robust methods and to the bootstrap. * Simultaneous confidence intervals: Bonferroni, Scheffe, Tukey, and Bechhofer * Analysis of variance, with two- and three-way analysis of variance * Random component models, nested designs, and balanced incomplete block designs * Analysis of frequency data through log-linear models, with emphasis on vector space viewpoint. This chapter alone is sufficient for a course on the analysis of frequency data.

Stochastic Dynamic Programming and the Control of Queueing Systems

Author: Linn I. Sennott

Publisher: John Wiley & Sons

ISBN: 0470317876

Category: Mathematics

Page: 354

View: 1508

A path-breaking account of Markov decision processes-theory and computation This book's clear presentation of theory, numerous chapter-end problems, and development of a unified method for the computation of optimal policies in both discrete and continuous time make it an excellent course text for graduate students and advanced undergraduates. Its comprehensive coverage of important recent advances in stochastic dynamic programming makes it a valuable working resource for operations research professionals, management scientists, engineers, and others. Stochastic Dynamic Programming and the Control of Queueing Systems presents the theory of optimization under the finite horizon, infinite horizon discounted, and average cost criteria. It then shows how optimal rules of operation (policies) for each criterion may be numerically determined. A great wealth of examples from the application area of the control of queueing systems is presented. Nine numerical programs for the computation of optimal policies are fully explicated. The Pascal source code for the programs is available for viewing and downloading on the Wiley Web site at www.wiley.com/products/subject/mathematics. The site contains a link to the author's own Web site and is also a place where readers may discuss developments on the programs or other aspects of the material. The source files are also available via ftp at ftp://ftp.wiley.com/public/sci_tech_med/stochastic Stochastic Dynamic Programming and the Control of Queueing Systems features: * Path-breaking advances in Markov decision process techniques, brought together for the first time in book form * A theorem/proof format (proofs may be omitted without loss of continuity) * Development of a unified method for the computation of optimal rules of system operation * Numerous examples drawn mainly from the control of queueing systems * Detailed discussions of nine numerical programs * Helpful chapter-end problems * Appendices with complete treatment of background material

The Association Graph and the Multigraph for Loglinear Models

Author: Harry J. Khamis

Publisher: SAGE

ISBN: 1452238952

Category: Mathematics

Page: 136

View: 1416

The Association Graph and the Multigraph for Loglinear Models will help students, particularly those studying the analysis of categorical data, to develop the ability to evaluate and unravel even the most complex loglinear models without heavy calculations or statistical software. This supplemental text reviews loglinear models, explains the association graph, and introduces the multigraph to students who may have little prior experience of graphical techniques, but have some familiarity with categorical variable modeling. The author presents logical step-by-step techniques from the point of view of the practitioner, focusing on how the technique is applied to contingency table data and how the results are interpreted.

The Analysis of Covariance and Alternatives

Statistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies

Author: Bradley Huitema

Publisher: John Wiley & Sons

ISBN: 9781118067468

Category: Mathematics

Page: 480

View: 5823

A complete guide to cutting-edge techniques and best practices for applying covariance analysis methods The Second Edition of Analysis of Covariance and Alternatives sheds new light on its topic, offering in-depth discussions of underlying assumptions, comprehensive interpretations of results, and comparisons of distinct approaches. The book has been extensively revised and updated to feature an in-depth review of prerequisites and the latest developments in the field. The author begins with a discussion of essential topics relating to experimental design and analysis, including analysis of variance, multiple regression, effect size measures and newly developed methods of communicating statistical results. Subsequent chapters feature newly added methods for the analysis of experiments with ordered treatments, including two parametric and nonparametric monotone analyses as well as approaches based on the robust general linear model and reversed ordinal logistic regression. Four groundbreaking chapters on single-case designs introduce powerful new analyses for simple and complex single-case experiments. This Second Edition also features coverage of advanced methods including: Simple and multiple analysis of covariance using both the Fisher approach and the general linear model approach Methods to manage assumption departures, including heterogeneous slopes, nonlinear functions, dichotomous dependent variables, and covariates affected by treatments Power analysis and the application of covariance analysis to randomized-block designs, two-factor designs, pre- and post-test designs, and multiple dependent variable designs Measurement error correction and propensity score methods developed for quasi-experiments, observational studies, and uncontrolled clinical trials Thoroughly updated to reflect the growing nature of the field, Analysis of Covariance and Alternatives is a suitable book for behavioral and medical scineces courses on design of experiments and regression and the upper-undergraduate and graduate levels. It also serves as an authoritative reference work for researchers and academics in the fields of medicine, clinical trials, epidemiology, public health, sociology, and engineering.

Simulation

A Modeler's Approach

Author: James R. Thompson

Publisher: John Wiley & Sons

ISBN: 0470317906

Category: Mathematics

Page: 328

View: 4576

A unique, integrated treatment of computer modeling and simulation "The future of science belongs to those willing to make the shift to simulation-based modeling," predicts Rice Professor James Thompson, a leading modeler and computational statistician widely known for his original ideas and engaging style. He discusses methods, available to anyone with a fast desktop computer, for integrating simulation into the modeling process in order to create meaningful models of real phenomena. Drawing from a wealth of experience, he gives examples from trading markets, oncology, epidemiology, statistical process control, physics, public policy, combat, real-world optimization, Bayesian analyses, and population dynamics. Dr. Thompson believes that, so far from liberating us from the necessity of modeling, the fast computer enables us to engage in realistic models of processes in , for example, economics, which have not been possible earlier because simple stochastic models in the forward temporal direction generally become quite unmanageably complex when one is looking for such things as likelihoods. Thompson shows how simulation may be used to bypass the necessity of obtaining likelihood functions or moment-generating functions as a precursor to parameter estimation. Simulation: A Modeler's Approach is a provocative and practical guide for professionals in applied statistics as well as engineers, scientists, computer scientists, financial analysts, and anyone with an interest in the synergy between data, models, and the digital computer.

Introduction to Statistical Time Series

Author: Wayne A. Fuller

Publisher: John Wiley & Sons

ISBN: 0470317752

Category: Mathematics

Page: 728

View: 2606

The subject of time series is of considerable interest, especially among researchers in econometrics, engineering, and the natural sciences. As part of the prestigious Wiley Series in Probability and Statistics, this book provides a lucid introduction to the field and, in this new Second Edition, covers the important advances of recent years, including nonstationary models, nonlinear estimation, multivariate models, state space representations, and empirical model identification. New sections have also been added on the Wold decomposition, partial autocorrelation, long memory processes, and the Kalman filter. Major topics include: * Moving average and autoregressive processes * Introduction to Fourier analysis * Spectral theory and filtering * Large sample theory * Estimation of the mean and autocorrelations * Estimation of the spectrum * Parameter estimation * Regression, trend, and seasonality * Unit root and explosive time series To accommodate a wide variety of readers, review material, especially on elementary results in Fourier analysis, large sample statistics, and difference equations, has been included.

Principles and Practice of Research

Strategies for Surgical Investigators

Author: H. Troidl,W.O. Spitzer,B. McPeek,D.S. Mulder,Martin F. Bach

Publisher: Springer Science & Business Media

ISBN: 3642969429

Category: Medical

Page: 381

View: 6171

emerging on the surgical scene to challenge or For some readers, the title of this book will im thodoxy. Although these innovations are often mediately raise the question, what exactly is greeted with great optimism, a factual basis for meant by surgical research? In the very broadest that enthusiasm is sometimes far from secure sense the term can be taken to include all en and much further work is frequently required to deavors, however elementary or limited in discover whether we are dealing with genuine scope, to advance surgical knowledge. Ideally, advances or not. it refers to well-organized attempts to establish The most exciting and attractive scenario for on a proper scientific basis, i. e. , to place beyond surgical research is unquestionably one that de reasonable doubt, the truth or otherwise of any picts a successful attempt by a researcher to es concepts, old or new, within the ambit of sur gery, and, of course, anaesthesia. tablish the accuracy of some bold innovation for which he himself is responsible. Joseph Lister, The methods used to achieve that end vary demonstrating by clinical trial that wound sup enormously, depending on the issue being in vestigated.

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: 1263

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.

The EM Algorithm and Extensions

Author: Geoffrey McLachlan,Thriyambakam Krishnan

Publisher: John Wiley & Sons

ISBN: 0470191600

Category: Mathematics

Page: 384

View: 3801

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.

Subjective and Objective Bayesian Statistics

Principles, Models, and Applications

Author: S. James Press

Publisher: John Wiley & Sons

ISBN: 0470317949

Category: Mathematics

Page: 600

View: 6435

Shorter, more concise chapters provide flexible coverage of the subject. Expanded coverage includes: uncertainty and randomness, prior distributions, predictivism, estimation, analysis of variance, and classification and imaging. Includes topics not covered in other books, such as the de Finetti Transform. Author S. James Press is the modern guru of Bayesian statistics.