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Multiple Comparisons, Selection and Applications in Biometry
by Fred. M. HoppeAims to provide in-depth descriptions of the latest developments in multiple comparison methods and selection procedures, while emphasizing biometry. This text is published in honour of the 70th birthday of Charles W. Dunnett - a pioneer in statistical methodology.
Multiple Comparisons: Theory and Methods
by Jason HsuMultiple Comparisons introduces simultaneous statistical inference and covers the theory and techniques for all-pairwise comparisons, multiple comparisons with the best, and multiple comparisons with a control. The author describes confidence intervals methods and stepwise exposes abuses and misconceptions, and guides readers to the correct method
Multiple Correspondence Analysis (Quantitative Applications in the Social Sciences)
by Brigitte Le Roux Henry RouanetRequiring no prior knowledge of correspondence analysis, this text provides a nontechnical introduction to Multiple Correspondence Analysis (MCA) as a method in its own right. The authors, Brigitte LeRoux and Henry Rouanet, present thematerial in a practical manner, keeping the needs of researchers foremost in mind.Key FeaturesReaders learn how to construct geometric spaces from relevant data, formulate questions of interest, and link statistical interpretation to geometric representations.They also learn how to perform structured data analysis and to draw inferential conclusions from MCA.The text uses real examples to help explain concepts.The authors stress the distinctive capacity of MCA to handle full-scale research studies.This supplementary text is appropriate for any graduate-level, intermediate, or advanced statistics course across the social and behavioral sciences, as well as for individual researchers.
Multiple Correspondence Analysis and Related Methods (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
by Michael Greenacre Jörg BlasiusAs a generalization of simple correspondence analysis, multiple correspondence analysis (MCA) is a powerful technique for handling larger, more complex datasets, including the high-dimensional categorical data often encountered in the social sciences, marketing, health economics, and biomedical research. Until now, however, the literature on the su
Multiple Criteria Decision Aid: Methods, Examples and Python Implementations (Springer Optimization and Its Applications #136)
by Jason Papathanasiou Nikolaos PloskasMultiple criteria decision aid (MCDA) methods are illustrated in this book through theoretical and computational techniques utilizing Python. Existing methods are presented in detail with a step by step learning approach. Theoretical background is given for TOPSIS, VIKOR, PROMETHEE, SIR, AHP, goal programming, and their variations. Comprehensive numerical examples are also discussed for each method in conjunction with easy to follow Python code. Extensions to multiple criteria decision making algorithms such as fuzzy number theory and group decision making are introduced and implemented through Python as well. Readers will learn how to implement and use each method based on the problem, the available data, the stakeholders involved, and the various requirements needed. Focusing on the practical aspects of the multiple criteria decision making methodologies, this book is designed for researchers, practitioners and advanced graduate students in the applied mathematics, information systems, operations research and business administration disciplines, as well as other engineers and scientists oriented in interdisciplinary research. Readers will greatly benefit from this book by learning and applying various MCDM/A methods. (Adiel Teixeira de Almeida, CDSID-Center for Decision System and Information Development, Universidade Federal de Pernambuco, Recife, Brazil) Promoting the development and application of multicriteria decision aid is essential to ensure more ethical and sustainable decisions. This book is a great contribution to this objective. It is a perfect blend of theory and practice, providing potential users and researchers with the theoretical bases of some of the best-known methods as well as with the computing tools needed to practice, to compare and to put these methods to use. (Jean-Pierre Brans, Vrije Universiteit Brussel, Brussels, Belgium) This book is intended for researchers, practitioners and students alike in decision support who wish to familiarize themselves quickly and efficiently with multicriteria decision aiding algorithms. The proposed approach is original, as it presents a selection of methods from the theory to the practical implementation in Python, including a detailed example. This will certainly facilitate the learning of these techniques, and contribute to their effective dissemination in applications. (Patrick Meyer, IMT Atlantique, Lab-STICC, Univ. Bretagne Loire, Brest, France)
Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems
by Theodor Stewart Jyrki Wallenius Boris Naujoks Matthias EhrgottIn the twenty-first century the sustainability of energy and transportation systems is on the top of the political agenda in many countries around the world. Environmental impacts of human economic activity necessitate the consideration of conflicting goals in decision making processes to develop sustainable systems. Any sustainable development has to reconcile conflicting economic and environmental objectives and criteria. The science of multiple criteria decision making has a lot to offer in addressing this need. Decision making with multiple (conflicting) criteria is the topic of research that is at the heart of the International Society of Multiple Criteria Decision Making. This book is based on selected papers presented at the societies 19th International Conference, held at The University of Auckland, New Zealand, from 7th to 12th January 2008 under the theme "MCDM for Sustainable Energy and Transportation Systems''.
Multiple Criteria Decision Making with Fuzzy Sets: MS Excel® and Other Software Solutions (Multiple Criteria Decision Making)
by Gerhard-Wilhelm Weber Babek ErdebilliUsing numerical examples to illustrate their concepts and results, this book examines recently developed fuzzy multi-criteria methods, such as Intuitionistic Fuzzy TOPSIS, Intuitionistic Fuzzy TOPSIS & DEA-AHP, Intuitionistic VIKOR, Pythagorean WASPAS, Pythagorean ENTROPI, Hesitant CBD, Hesitant MABAC, Triangular EDAS, Triangular PROMETHEE, q-Rung Orthopair COPRAS, and Fuzzy Type – 2 ELECTRE. Each chapter covers practical applications in addition to fresh developments and results. Given its structure and scope, the book can be used as a textbook in graduate courses on operations research and industrial engineering. It also offers a valuable resource for scientists working in a range of disciplines that require multi-criteria decision making.
Multiple Decrement Models in Insurance: An Introduction Using R
by Shailaja Rajendra DeshmukhThe book will serve as a guide to many actuarial concepts and statistical techniques in multiple decrement models and their application in calculation of premiums and reserves in life insurance products with riders and in pension and employee benefit plans as in these schemes, the benefit paid on termination of employment depends upon the several causes of termination. Multiple state models are discussed to accommodate the insurance products in which the payment of benefits or premiums is dependent on being in a given state or moving between a given pair of states at a given time, for example, disability income insurance model. The book also discusses stochastic models for interest rates and calculation of premiums for some products in this set up. The highlight of the book is usage of R software, freely available from public domain, for computations of various monetary functions involved in insurance business. R commands are given for all the computations.
Multiple Dirichlet Series, L-functions and Automorphic Forms
by Daniel Bump Solomon Friedberg Dorian GoldfeldMultiple Dirichlet Series, L-functions and Automorphic Forms gives the latest advances in the rapidly developing subject of Multiple Dirichlet Series, an area with origins in the theory of automorphic forms that exhibits surprising and deep connections to crystal graphs and mathematical physics. As such, it represents a new way in which areas including number theory, combinatorics, statistical mechanics, and quantum groups are seen to fit together. The volume also includes papers on automorphic forms and L-functions and related number-theoretic topics. This volume will be a valuable resource for graduate students and researchers in number theory, combinatorics, representation theory, mathematical physics, and special functions. Contributors: J. Beineke, B. Brubaker, D. Bump, G. Chinta, G. Cornelissen, C.A. Diaconu, S. Frechette, S. Friedberg, P. Garrett, D. Goldfeld, P.E. Gunnells, B. Heim, J. Hundley, D. Ivanov, Y. Komori, A.V. Kontorovich, O. Lorscheid, K. Matsumoto, P.J. McNamara, S.J. Patterson, M. Suzuki, H. Tsumura.
Multiple Factor Analysis by Example Using R: Sense, Sentimentality And The Soldier-horse Relationship In The Great War (Chapman And Hall/crc The R Ser. #83)
by Jérôme PagèsMultiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of this methodology, Multiple Factor Analysis by Example Using R brings together the theoretical and methodological aspects of MFA. It also inc
Multiple Fixed-Point Theorems and Applications in the Theory of ODEs, FDEs and PDEs (Chapman & Hall/CRC Monographs and Research Notes in Mathematics)
by Svetlin G. Georgiev Khaled ZennirMultiple Fixed-Point Theorems and Applications in the Theory of ODEs, FDEs and PDEs covers all the basics of the subject of fixed-point theory and its applications with a strong focus on examples, proofs and practical problems, thus making it ideal as course material but also as a reference for self-study. Many problems in science lead to nonlinear equations T x + F x = x posed in some closed convex subset of a Banach space. In particular, ordinary, fractional, partial differential equations and integral equations can be formulated like these abstract equations. It is desirable to develop fixed-point theorems for such equations. In this book, the authors investigate the existence of multiple fixed points for some operators that are of the form T + F, where T is an expansive operator and F is a k-set contraction. This book offers the reader an overview of recent developments of multiple fixed-point theorems and their applications. About the Authors Svetlin G. Georgiev is a mathematician who has worked in various areas of mathematics. He currently focuses on harmonic analysis, functional analysis, partial differential equations, ordinary differential equations, Clifford and quaternion analysis, integral equations and dynamic calculus on time scales. Khaled Zennir is assistant professor at Qassim University, KSA. He received his PhD in mathematics in 2013 from Sidi Bel Abbès University, Algeria. He obtained his Habilitation in mathematics from Constantine University, Algeria in 2015. His research interests lie in nonlinear hyperbolic partial differential equations: global existence, blow up and long-time behavior.
Multiple Imputation and its Application
by Michael Kenward James CarpenterA practical guide to analysing partially observed data.Collecting, analysing and drawing inferences from data is central to research in the medical and social sciences. Unfortunately, it is rarely possible to collect all the intended data. The literature on inference from the resulting incomplete data is now huge, and continues to grow both as methods are developed for large and complex data structures, and as increasing computer power and suitable software enable researchers to apply these methods.This book focuses on a particular statistical method for analysing and drawing inferences from incomplete data, called Multiple Imputation (MI). MI is attractive because it is both practical and widely applicable. The authors aim is to clarify the issues raised by missing data, describing the rationale for MI, the relationship between the various imputation models and associated algorithms and its application to increasingly complex data structures.Multiple Imputation and its Application:Discusses the issues raised by the analysis of partially observed data, and the assumptions on which analyses rest.Presents a practical guide to the issues to consider when analysing incomplete data from both observational studies and randomized trials.Provides a detailed discussion of the practical use of MI with real-world examples drawn from medical and social statistics.Explores handling non-linear relationships and interactions with multiple imputation, survival analysis, multilevel multiple imputation, sensitivity analysis via multiple imputation, using non-response weights with multiple imputation and doubly robust multiple imputation.Multiple Imputation and its Application is aimed at quantitative researchers and students in the medical and social sciences with the aim of clarifying the issues raised by the analysis of incomplete data data, outlining the rationale for MI and describing how to consider and address the issues that arise in its application.
Multiple Imputation and its Application (Statistics in Practice)
by James R. Carpenter Michael G. Kenward Jonathan W. Bartlett Tim P. Morris Angela M. Wood Matteo QuartagnoMultiple Imputation and its Application The most up-to-date edition of a bestselling guide to analyzing partially observed data In this comprehensively revised Second Edition of Multiple Imputation and its Application, a team of distinguished statisticians delivers an overview of the issues raised by missing data, the rationale for multiple imputation as a solution, and the practicalities of applying it in a multitude of settings. With an accessible and carefully structured presentation aimed at quantitative researchers, Multiple Imputation and its Application is illustrated with a range of examples and offers key mathematical details. The book includes a wide range of theoretical and computer-based exercises, tested in the classroom, which are especially useful for users of R or Stata. Readers will find: A comprehensive overview of one of the most effective and popular methodologies for dealing with incomplete data sets Careful discussion of key concepts A range of examples illustrating the key ideas Practical advice on using multiple imputation Exercises and examples designed for use in the classroom and/or private study Written for applied researchers looking to use multiple imputation with confidence, and for methods researchers seeking an accessible overview of the topic, Multiple Imputation and its Application will also earn a place in the libraries of graduate students undertaking quantitative analyses.
Multiple Imputation in Practice: With Examples Using IVEware
by Trivellore Raghunathan Patricia A. Berglund Peter W. SolenbergerMultiple Imputation in Practice: With Examples Using IVEware provides practical guidance on multiple imputation analysis, from simple to complex problems using real and simulated data sets. Data sets from cross-sectional, retrospective, prospective and longitudinal studies, randomized clinical trials, complex sample surveys are used to illustrate both simple, and complex analyses. Version 0.3 of IVEware, the software developed by the University of Michigan, is used to illustrate analyses. IVEware can multiply impute missing values, analyze multiply imputed data sets, incorporate complex sample design features, and be used for other statistical analyses framed as missing data problems. IVEware can be used under Windows, Linux, and Mac, and with software packages like SAS, SPSS, Stata, and R, or as a stand-alone tool. This book will be helpful to researchers looking for guidance on the use of multiple imputation to address missing data problems, along with examples of correct analysis techniques.
Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies
by Yulei He Guangyu Zhang Chiu-Hsieh HsuMultiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple imputation approach to missing data problems that are often encountered in data analysis. Over the past 40 years or so, multiple imputation has gone through rapid development in both theories and applications. It is nowadays the most versatile, popular, and effective missing-data strategy that is used by researchers and practitioners across different fields. There is a strong need to better understand and learn about multiple imputation in the research and practical community. Accessible to a broad audience, this book explains statistical concepts of missing data problems and the associated terminology. It focuses on how to address missing data problems using multiple imputation. It describes the basic theory behind multiple imputation and many commonly-used models and methods. These ideas are illustrated by examples from a wide variety of missing data problems. Real data from studies with different designs and features (e.g., cross-sectional data, longitudinal data, complex surveys, survival data, studies subject to measurement error, etc.) are used to demonstrate the methods. In order for readers not only to know how to use the methods, but understand why multiple imputation works and how to choose appropriate methods, simulation studies are used to assess the performance of the multiple imputation methods. Example datasets and sample programming code are either included in the book or available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book). Key Features Provides an overview of statistical concepts that are useful for better understanding missing data problems and multiple imputation analysis Provides a detailed discussion on multiple imputation models and methods targeted to different types of missing data problems (e.g., univariate and multivariate missing data problems, missing data in survival analysis, longitudinal data, complex surveys, etc.) Explores measurement error problems with multiple imputation Discusses analysis strategies for multiple imputation diagnostics Discusses data production issues when the goal of multiple imputation is to release datasets for public use, as done by organizations that process and manage large-scale surveys with nonresponse problems For some examples, illustrative datasets and sample programming code from popular statistical packages (e.g., SAS, R, WinBUGS) are included in the book. For others, they are available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book)
Multiple Instance Learning
by Sebastián Ventura Francisco Herrera Rafael Bello Chris Cornelis Amelia Zafra Dánel Sánchez-Tarragó Sarah VluymansThis book provides a general overview of multiple instance learning (MIL), defining the framework and covering the central paradigms. The authors discuss the most important algorithms for MIL such as classification, regression and clustering. With a focus on classification, a taxonomy is set and the most relevant proposals are specified. Efficient algorithms are developed to discover relevant information when working with uncertainty. Key representative applications are included. This book carries out a study of the key related fields of distance metrics and alternative hypothesis. Chapters examine new and developing aspects of MIL such as data reduction for multi-instance problems and imbalanced MIL data. Class imbalance for multi-instance problems is defined at the bag level, a type of representation that utilizes ambiguity due to the fact that bag labels are available, but the labels of the individual instances are not defined. Additionally, multiple instance multiple label learning is explored. This learning framework introduces flexibility and ambiguity in the object representation providing a natural formulation for representing complicated objects. Thus, an object is represented by a bag of instances and is allowed to have associated multiple class labels simultaneously. This book is suitable for developers and engineers working to apply MIL techniques to solve a variety of real-world problems. It is also useful for researchers or students seeking a thorough overview of MIL literature, methods, and tools.
Multiple Perspectives on Difficulties in Learning Literacy and Numeracy
by Claire Wyatt-Smith Stephanie Gunn John ElkinsThere are many approaches to researching the difficulties in learning that students experience in the key areas of literacy and numeracy. This book seeks to advance understanding of these difficulties and the interventions that have been used to improve outcomes. The book addresses the sometimes complementary and sometimes contradictory results, and generates new approaches to understanding and serving students with difficulties in literacy and numeracy. The book represents a departure from conventional wisdom as most scholars and graduate students draw upon ideas from only one of the three domains focal in the book and usually from one single or dominant theoretical frame. Typically, readers will affiliate with reading education, mathematics education, or learning disabilities and belong to one of the corresponding professional associations such as IRA, NCTM, or CLD. This book's scope will open a scholarly forum for engaging readers with a familiarity with one of these domains while providing insight into the others on offer in the book.
Multiple Regression: A Practical Introduction
by John M. Roberts Aki RobertsMultiple Regression: A Practical Introduction is a text for an advanced undergraduate or beginning graduate course in statistics for social science and related fields. Also, students preparing for more advanced courses can self-study the text to refresh and solidify their statistical background. Drawing on decades of teaching this material, the authors present the ideas in an approachable and nontechnical manner, with no expectation that readers have more than a standard introductory statistics course as background. Multiple regression asks how a dependent variable is related to, or predicted by, a set of independent variables. The book includes many interesting example analyses and interpretations, along with exercises. Each dataset used for the examples and exercises is small enough for readers to easily grasp the entire dataset and its analysis with respect to the specific statistical techniques covered. SPSS, Stata, SAS, and R code and commands for each type of analysis or recoding of variables in the book are available on an accompanying website, along with solutions to the exercises (on the instructor site).
Multiple Regression: A Practical Introduction
by John M. Roberts Aki RobertsMultiple Regression: A Practical Introduction is a text for an advanced undergraduate or beginning graduate course in statistics for social science and related fields. Also, students preparing for more advanced courses can self-study the text to refresh and solidify their statistical background. Drawing on decades of teaching this material, the authors present the ideas in an approachable and nontechnical manner, with no expectation that readers have more than a standard introductory statistics course as background. Multiple regression asks how a dependent variable is related to, or predicted by, a set of independent variables. The book includes many interesting example analyses and interpretations, along with exercises. Each dataset used for the examples and exercises is small enough for readers to easily grasp the entire dataset and its analysis with respect to the specific statistical techniques covered. SPSS, Stata, SAS, and R code and commands for each type of analysis or recoding of variables in the book are available on an accompanying website, along with solutions to the exercises (on the instructor site).
Multiple Scattering Theory for Spectroscopies: A Guide To Multiple Scattering Computer Codes -- Dedicated To C. R. Natoli On The Occasion Of His 75th Birthday (Springer Proceedings In Physics #204)
by Didier Sébilleau Keisuke Hatada Hubert EbertThis edited book, based on material presented at the EU Spec Training School on Multiple Scattering Codes and the following MSNano Conference, is divided into two distinct parts. The first part, subtitled “basic knowledge”, provides the basics of the multiple scattering description in spectroscopies, enabling readers to understand the physics behind the various multiple scattering codes available for modelling spectroscopies. The second part, “extended knowledge”, presents “state- of-the-art” short chapters on specific subjects associated with improving of the actual description of spectroscopies within the multiple scattering formalism, such as inelastic processes, or precise examples of modelling.
Multiple Sequence Alignments: Which Program Fits My Data?
by Theodor SperleaThis book is a practical guide for biologists who use multiple sequence alignments (MSAs) for their data analysis and are looking for a comprehensive overview of the many different programs. Despite their important role in data analysis, there is uncertainty among researchers about exactly how MSA programs work - not to mention how and why the different analyzes lead to different results. Which program is the right one for evaluating my data and how can I ensure that I have drawn all relevant findings from the alignments? This book offers helpful explanations and background information without requiring extensive bioinformatics knowledge and slowly introduces the reader to the topic.In the first part of the book, the possible fields of application as well as the formats that are usually produced by MSA programs are described in detail. The central algorithms as well as the internal processes of the most common MSA programs of the past and the present are also explained in an uncomplicated manner in greater detail. The second part of the book is a detailed, data-based comparison between MSA programs, which is intended to help you decide which program to use for your next alignment.
Multiple Sequenzalignments: Welches Programm passt zu meinen Daten?
by Theodor SperleaDieses Buch ist ein praktischer Ratgeber für Biologinnen und Biologen, die Multiple Sequenzalignments (MSAs) für ihre Datenanalysen verwenden und einen verständlichen Überblick über die vielen verschiedenen Programme suchen. Trotz ihres wichtigen Stellenwertes in der Datenanalyse herrscht Unsicherheit unter den Forschenden wie MSA-Programme genau funktionieren - ganz zu schweigen davon, wie und warum die unterschiedlichen Analysen zu verschiedenen Ergebnissen führen. Welches Programm ist für die Auswertung meiner Daten das richtige und wie kann ich sicherstellen, alle relevanten Erkenntnisse aus den Alignments gezogen zu haben? Dieses Buch bietet hilfreiche Erklärungen und Hintergründe ohne große bioinformatische Vorkenntnisse vorauszusetzen und führt die Leserinnen und Leser langsam in die Thematik ein.Im ersten Teil des Buches werden die möglichen Einsatzfelder wie auch die Formate, die üblicherweise von MSA-Programmen produziert werden, im Detail beschrieben. Ebenso werden auf unkomplizierte Weise die zentralen Algorithmen sowie die inneren Abläufe der gängigsten MSA-Programme der Vergangenheit und der Gegenwart in größerer Detailtiefe erklärt. Den zweiten Teil des Buches bildet ein ausführlicher datenbasierter Vergleich zwischen MSA-Programmen, der als Entscheidungshilfe für die Programmauswahl für dein nächstes Alignment dienen soll.
Multiple Shooting and Time Domain Decomposition Methods
by Rolf Rannacher Stefan Körkel Thomas Carraro Michael GeigerThis book offers a comprehensive collection of the most advanced numerical techniques for the efficient and effective solution of simulation and optimization problems governed by systems of time-dependent differential equations. The contributions present various approaches to time domain decomposition, focusing on multiple shooting and parareal algorithms. The range of topics covers theoretical analysis of the methods, as well as their algorithmic formulation and guidelines for practical implementation. Selected examples show that the discussed approaches are mandatory for the solution of challenging practical problems. The practicability and efficiency of the presented methods is illustrated by several case studies from fluid dynamics, data compression, image processing and computational biology, giving rise to possible new research topics. This volume, resulting from the workshop Multiple Shooting and Time Domain Decomposition Methods, held in Heidelberg in May 2013, will be of great interest to applied mathematicians, computer scientists and all scientists using mathematical methods.
Multiple Stopping Problems: Uni- and Multilateral Approaches
by Krzysztof Szajowski Georgy SofronovThis book presents the theory of rational decisions involving the selection of stopping times in observed discrete-time stochastic processes, both by single and multiple decision-makers. Readers will become acquainted with the models, strategies, and applications of these models.It begins with an examination of selected models framed as stochastic optimization challenges, emphasizing the critical role of optimal stopping times in sequential statistical procedures. The authors go on to explore models featuring multiple stopping and shares on leading applications, particularly focusing on change point detection, selection problems, and the nuances of behavioral ecology. In the following chapters, an array of perspectives on model strategies is presented, elucidating their interpretation and the methodologies underpinning their genesis. Essential notations and definitions are introduced, examining general theorems about solution existence and structure, with an intricate analysis of optimal stopping predicaments and addressing crucial multilateral models. The reader is presented with the practical application of models based on multiple stopping within stochastic processes. The coverage includes a diverse array of domains, including sequential statistics, finance, economics, and the broader generalization of the best-choice problem. Additionally, it delves into numerical and asymptotic solutions, offering a comprehensive exploration of optimal stopping quandaries.The book will be of interest to researchers and practitioners in fields such as economics, finance, and engineering. It could also be used by graduate students doing a research degree in insurance, economics or business analytics or an advanced undergraduate course in mathematical sciences.
Multiple Testing Problems in Pharmaceutical Statistics (Chapman & Hall/CRC Biostatistics Series)
by Ajit C. Tamhane Alex Dmitrienko Frank BretzUseful Statistical Approaches for Addressing Multiplicity IssuesIncludes practical examples from recent trials Bringing together leading statisticians, scientists, and clinicians from the pharmaceutical industry, academia, and regulatory agencies, Multiple Testing Problems in Pharmaceutical Statistics explores the rapidly growing area of multiple c