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Author: Samaradasa Weerahandi
Publisher: John Wiley & Sons
ISBN: 9780471470175
Category : Mathematics
Languages : en
Pages : 382
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Book Description
A complete guide to powerful and practical statistical modeling using MANOVA Numerous statistical applications are time dependent. Virtually all biomedical, pharmaceutical, and industrial experiments demand repeated measurements over time. The same holds true for market research and analysis. Yet conventional methods, such as the Repeated Measures Analysis of Variance (Rm ANOVA), do not always yield exact solutions, obliging practitioners to settle for asymptotic results and approximate solutions. Generalized inference in Multivariate Analysis of Variance (MANOVA), mixed models, and growth curves offer exact methods of data analysis under milder conditions without deviating from the conventional philosophy of statistical inference. Generalized Inference in Repeated Measures is a concise, self-contained guide to the use of these innovative solutions, presenting them as extensions of–rather than alternatives to–classical methods of statistical evaluation. Requiring minimal prior knowledge of statistical concepts in the evaluation of linear models, the book provides exact parametric methods for each application considered, with solutions presented in terms of generalized p-values. Coverage includes: New concepts in statistical inference, with special focus on generalized p-values and generalized confidence intervals One-way and two-way ANOVA, in cases of equal and unequal variances Basic and higher-way mixed models, including testing and estimation of fixed effects and variance components Multivariate populations, including basic inference, comparison, and analysis of variance Basic, widely used repeated measures models including crossover designs and growth curves With a comprehensive set of formulas, illustrative examples, and exercises in each chapter, Generalized Inference in Repeated Measures is ideal as both a comprehensive reference for research professionals and a text for students.
Author: Samaradasa Weerahandi
Publisher: John Wiley & Sons
ISBN: 9780471470175
Category : Mathematics
Languages : en
Pages : 382
Get Book Here
Book Description
A complete guide to powerful and practical statistical modeling using MANOVA Numerous statistical applications are time dependent. Virtually all biomedical, pharmaceutical, and industrial experiments demand repeated measurements over time. The same holds true for market research and analysis. Yet conventional methods, such as the Repeated Measures Analysis of Variance (Rm ANOVA), do not always yield exact solutions, obliging practitioners to settle for asymptotic results and approximate solutions. Generalized inference in Multivariate Analysis of Variance (MANOVA), mixed models, and growth curves offer exact methods of data analysis under milder conditions without deviating from the conventional philosophy of statistical inference. Generalized Inference in Repeated Measures is a concise, self-contained guide to the use of these innovative solutions, presenting them as extensions of–rather than alternatives to–classical methods of statistical evaluation. Requiring minimal prior knowledge of statistical concepts in the evaluation of linear models, the book provides exact parametric methods for each application considered, with solutions presented in terms of generalized p-values. Coverage includes: New concepts in statistical inference, with special focus on generalized p-values and generalized confidence intervals One-way and two-way ANOVA, in cases of equal and unequal variances Basic and higher-way mixed models, including testing and estimation of fixed effects and variance components Multivariate populations, including basic inference, comparison, and analysis of variance Basic, widely used repeated measures models including crossover designs and growth curves With a comprehensive set of formulas, illustrative examples, and exercises in each chapter, Generalized Inference in Repeated Measures is ideal as both a comprehensive reference for research professionals and a text for students.
Author: Garrett Fitzmaurice
Publisher: CRC Press
ISBN: 142001157X
Category : Mathematics
Languages : en
Pages : 633
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Book Description
Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory
Author: David C. Hoaglin
Publisher: John Wiley & Sons
ISBN: 1118150694
Category : Mathematics
Languages : en
Pages : 564
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Book Description
WILEY-INTERSCIENCE PAPERBACK SERIES 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. "Exploring Data Tables, Trends, and Shapes (EDTTS) was written as a companion volume to the same editors' book, Understanding Robust and Exploratory Data Analysis (UREDA). Whereas UREDA is a collection of exploratory and resistant methods of estimation and display, EDTTS goes a step further, describing multivariate and more complicated techniques . . . I feel that the authors have made a very significant contribution in the area of multivariate nonparametric methods. This book [is] a valuable source of reference to researchers in the area." —Technometrics "This edited volume . . . provides an important theoretical and philosophical extension to the currently popular statistical area of Exploratory Data Analysis, which seeks to reveal structure, or simple descriptions, in data . . . It is . . . an important reference volume which any statistical library should consider seriously." —The Statistician This newly available and affordably priced paperback version of Exploring Data Tables, Trends, and Shapes presents major advances in exploratory data analysis and robust regression methods and explains the techniques, relating them to classical methods. The book addresses the role of exploratory and robust techniques in the overall data-analytic enterprise, and it also presents new methods such as fitting by organized comparisons using the square combining table and identifying extreme cells in a sizable contingency table with probabilistic and exploratory approaches. The book features a chapter on using robust regression in less technical language than available elsewhere. Conceptual support for each technique is also provided.
Author: Ziheng Yang
Publisher: OUP Oxford
ISBN: 0191513768
Category : Science
Languages : en
Pages : 374
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Book Description
The field of molecular evolution has experienced explosive growth in recent years due to the rapid accumulation of genetic sequence data, continuous improvements to computer hardware and software, and the development of sophisticated analytical methods. The increasing availability of large genomic data sets requires powerful statistical methods to analyse and interpret them, generating both computational and conceptual challenges for the field. Computational Molecular Evolution provides an up-to-date and comprehensive coverage of modern statistical and computational methods used in molecular evolutionary analysis, such as maximum likelihood and Bayesian statistics. Yang describes the models, methods and algorithms that are most useful for analysing the ever-increasing supply of molecular sequence data, with a view to furthering our understanding of the evolution of genes and genomes. The book emphasizes essential concepts rather than mathematical proofs. It includes detailed derivations and implementation details, as well as numerous illustrations, worked examples, and exercises. It will be of relevance and use to students and professional researchers (both empiricists and theoreticians) in the fields of molecular phylogenetics, evolutionary biology, population genetics, mathematics, statistics and computer science. Biologists who have used phylogenetic software programs to analyze their own data will find the book particularly rewarding, although it should appeal to anyone seeking an authoritative overview of this exciting area of computational biology.
Author: John Geweke
Publisher: John Wiley & Sons
ISBN: 0471744727
Category : Mathematics
Languages : en
Pages : 322
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Book Description
Tools to improve decision making in an imperfect world This publication provides readers with a thorough understanding of Bayesian analysis that is grounded in the theory of inference and optimal decision making. Contemporary Bayesian Econometrics and Statistics provides readers with state-of-the-art simulation methods and models that are used to solve complex real-world problems. Armed with a strong foundation in both theory and practical problem-solving tools, readers discover how to optimize decision making when faced with problems that involve limited or imperfect data. The book begins by examining the theoretical and mathematical foundations of Bayesian statistics to help readers understand how and why it is used in problem solving. The author then describes how modern simulation methods make Bayesian approaches practical using widely available mathematical applications software. In addition, the author details how models can be applied to specific problems, including: * Linear models and policy choices * Modeling with latent variables and missing data * Time series models and prediction * Comparison and evaluation of models The publication has been developed and fine- tuned through a decade of classroom experience, and readers will find the author's approach very engaging and accessible. There are nearly 200 examples and exercises to help readers see how effective use of Bayesian statistics enables them to make optimal decisions. MATLAB? and R computer programs are integrated throughout the book. An accompanying Web site provides readers with computer code for many examples and datasets. This publication is tailored for research professionals who use econometrics and similar statistical methods in their work. With its emphasis on practical problem solving and extensive use of examples and exercises, this is also an excellent textbook for graduate-level students in a broad range of fields, including economics, statistics, the social sciences, business, and public policy.
Author: Andrew B. Lawson
Publisher: John Wiley & Sons
ISBN: 1118723171
Category : Medical
Languages : en
Pages : 302
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Book Description
Spatial epidemiology is the description and analysis of the geographical distribution of disease. It is more important now than ever, with modern threats such as bio-terrorism making such analysis even more complex. This second edition of Statistical Methods in Spatial Epidemiology is updated and expanded to offer a complete coverage of the analysis and application of spatial statistical methods. The book is divided into two main sections: Part 1 introduces basic definitions and terminology, along with map construction and some basic models. This is expanded upon in Part II by applying this knowledge to the fundamental problems within spatial epidemiology, such as disease mapping, ecological analysis, disease clustering, bio-terrorism, space-time analysis, surveillance and infectious disease modelling. Provides a comprehensive overview of the main statistical methods used in spatial epidemiology. Updated to include a new emphasis on bio-terrorism and disease surveillance. Emphasizes the importance of space-time modelling and outlines the practical application of the method. Discusses the wide range of software available for analyzing spatial data, including WinBUGS, SaTScan and R, and features an accompanying website hosting related software. Contains numerous data sets, each representing a different approach to the analysis, and provides an insight into various modelling techniques. This text is primarily aimed at medical statisticians, researchers and practitioners from public health and epidemiology. It is also suitable for postgraduate students of statistics and epidemiology, as well professionals working in government agencies.
Author: Bent Natvig
Publisher: John Wiley & Sons
ISBN: 0470977132
Category : Mathematics
Languages : en
Pages : 203
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Book Description
Most books in reliability theory are dealing with a description of component and system states as binary: functioning or failed. However, many systems are composed of multi-state components with different performance levels and several failure modes. There is a great need in a series of applications to have a more refined description of these states, for instance, the amount of power generated by an electrical power generation system or the amount of gas that can be delivered through an offshore gas pipeline network. This book provides a descriptive account of various types of multistate system, bound-for multistate systems, probabilistic modeling of monitoring and maintenance of multistate systems with components along with examples of applications. Key Features: Looks at modern multistate reliability theory with applications covering a refined description of components and system states. Presents new research, such as Bayesian assessment of system availabilities and measures of component importance. Complements the methodological description with two substantial case studies. Reliability engineers and students involved in the field of reliability, applied mathematics and probability theory will benefit from this book.
Author: Nozer D. Singpurwalla
Publisher: John Wiley & Sons
ISBN: 0470060336
Category : Mathematics
Languages : en
Pages : 396
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Book Description
We all like to know how reliable and how risky certain situations are, and our increasing reliance on technology has led to the need for more precise assessments than ever before. Such precision has resulted in efforts both to sharpen the notions of risk and reliability, and to quantify them. Quantification is required for normative decision-making, especially decisions pertaining to our safety and wellbeing. Increasingly in recent years Bayesian methods have become key to such quantifications. Reliability and Risk provides a comprehensive overview of the mathematical and statistical aspects of risk and reliability analysis, from a Bayesian perspective. This book sets out to change the way in which we think about reliability and survival analysis by casting them in the broader context of decision-making. This is achieved by: Providing a broad coverage of the diverse aspects of reliability, including: multivariate failure models, dynamic reliability, event history analysis, non-parametric Bayes, competing risks, co-operative and competing systems, and signature analysis. Covering the essentials of Bayesian statistics and exchangeability, enabling readers who are unfamiliar with Bayesian inference to benefit from the book. Introducing the notion of “composite reliability”, or the collective reliability of a population of items. Discussing the relationship between notions of reliability and survival analysis and econometrics and financial risk. Reliability and Risk can most profitably be used by practitioners and research workers in reliability and survivability as a source of information, reference, and open problems. It can also form the basis of a graduate level course in reliability and risk analysis for students in statistics, biostatistics, engineering (industrial, nuclear, systems), operations research, and other mathematically oriented scientists, wherein the instructor could supplement the material with examples and problems.
Author: Douglas C. Montgomery
Publisher: John Wiley & Sons
ISBN: 1118627369
Category : Mathematics
Languages : en
Pages : 749
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Book Description
Praise for the Fourth Edition "As with previous editions, the authors have produced a leading textbook on regression." —Journal of the American Statistical Association A comprehensive and up-to-date introduction to the fundamentals of regression analysis Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in today’s cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences. Following a general introduction to regression modeling, including typical applications, a host of technical tools are outlined such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book then discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. The Fifth Edition features numerous newly added topics, including: A chapter on regression analysis of time series data that presents the Durbin-Watson test and other techniques for detecting autocorrelation as well as parameter estimation in time series regression models Regression models with random effects in addition to a discussion on subsampling and the importance of the mixed model Tests on individual regression coefficients and subsets of coefficients Examples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction data. In addition to Minitab, SAS, and S-PLUS, the authors have incorporated JMP and the freely available R software to illustrate the discussed techniques and procedures in this new edition. Numerous exercises have been added throughout, allowing readers to test their understanding of the material. Introduction to Linear Regression Analysis, Fifth Edition is an excellent book for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. The book also serves as a valuable, robust resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.
Author: Peihua Qiu
Publisher: John Wiley & Sons
ISBN: 0471733164
Category : Mathematics
Languages : en
Pages : 344
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Book Description
The first text to bridge the gap between image processing andjump regression analysis Recent statistical tools developed to estimate jump curves andsurfaces have broad applications, specifically in the area of imageprocessing. Often, significant differences in technicalterminologies make communication between the disciplines of imageprocessing and jump regression analysis difficult. Ineasy-to-understand language, Image Processing and JumpRegression Analysis builds a bridge between the worlds ofcomputer graphics and statistics by addressing both the connectionsand the differences between these two disciplines. The authorprovides a systematic analysis of the methodology behindnonparametric jump regression analysis by outlining procedures thatare easy to use, simple to compute, and have proven statisticaltheory behind them. Key topics include: Conventional smoothing procedures Estimation of jump regression curves Estimation of jump location curves of regression surfaces Jump-preserving surface reconstruction based on localsmoothing Edge detection in image processing Edge-preserving image restoration With mathematical proofs kept to a minimum, this book isuniquely accessible to a broad readership. It may be used as aprimary text in nonparametric regression analysis and imageprocessing as well as a reference guide for academicians andindustry professionals focused on image processing or curve/surfaceestimation.
