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Applied Functional Analysis. Approximation Methods and Computers: Applied Functional Analysis, Approximation Methods and Computers
by S.S. KutateladzeThis book contains the most remarkable papers of L.V. Kantorovich in applied and numerical mathematics. It explores the principal directions of Kantorovich's research in approximate methods. The book covers descriptive set theory and functional analysis in semi-ordered vector spaces.
Applied Fuzzy Mathematics (Forum for Interdisciplinary Mathematics)
by Manoranjan Kumar SinghThis comprehensive introductory textbook is designed for undergraduate mathematics students who are interested in gaining an in-depth understanding of fuzzy mathematics and its applications. The book covers a wide range of topics, including fuzzy linear equations, fuzzy graphs, fuzzy measures, fuzzy logic, fuzzy topological spaces, fuzzy subgroups, as well as applications of fuzzy mathematics in various other fields. While readers are assumed to be familiar with the concept of fuzzy sets, the book maintains a clear and straightforward approach that makes it easy to follow for students at any level of proficiency. The advanced content is presented in an insightful and accessible manner, empowering students to apply the concepts they learn to real-world problems and applications. Enriched with over 105 solved examples, 133 challenging problems, 185 multiple-choice questions, and 150 true/false statements, this book is an ideal tool for beginners seeking to gain a solid foundation in fuzzy mathematics and its applications. Whether you are studying independently or as part of a classroom setting, this book provides a valuable resource that will help you achieve your goals.
Applied Game Theory and Strategic Behavior
by Ilhan K. Geckil Patrick L. AndersonUseful Tools to Help Solve Decision Making ProblemsApplied Game Theory and Strategic Behavior demonstrates the use of various game theory techniques to address practical business, economic, legal, and public policy issues. It also illustrates the benefits of employing strategic thinking that incorporates the uncertainty surrounding the behavior of
Applied Genetic Algorithm and Its Variants: Case Studies and New Developments (Springer Tracts in Nature-Inspired Computing)
by Nilanjan DeyThis book provides fundamental concepts related to various types of genetic algorithms and practical applications in various domains such as medical imaging, manufacturing, and engineering design. The book discusses genetic algorithms which are used to solve a variety of optimization problems. The genetic algorithms are demonstrated to offer reliable search in complex spaces. The book presents high-quality research work by academics and researchers which is useful for young researchers and students.
Applied Group Theory: For Physicists and Chemists (Dover Books on Physics)
by George H. DuffeyThis text introduces advanced undergraduates and graduate students to symmetry relations by means of group theory. Key relationships are derived in detail from first principles. Rather than matrix theory, the treatment employs algebraic theory in deriving the properties of characters and projection operators. This approach is customarily employed in quantum mechanics courses and makes the connection to group structure clearer. Cayley diagrams illustrate the structure of finite groups. Permutation groups are considered in some detail, and the special methods needed for continuous groups are developed.The treatment's broad range of applications offers students assistance in analyzing the modes of motion of symmetric classical systems; the constitutive relations in crystalline systems; the modes of vibration in molecules; the molecular orbitals of molecules; the electronic structures of atoms; the attendant spectra; and fundamental particle multiplets. Each chapter concludes with a concise review, discussion questions, problems, and references. 1992 edition.
Applied Health Economics (Routledge Advanced Texts in Economics and Finance)
by Andrew M. Jones Nigel Rice Teresa Bago D'Uva Silvia BaliaThe first edition of Applied Health Economics did an expert job of showing how the availability of large scale data sets and the rapid advancement of advanced econometric techniques can help health economists and health professionals make sense of information better than ever before. This second edition has been revised and updated throughout and includes a new chapter on the description and modelling of individual health care costs, thus broadening the book’s readership to those working on risk adjustment and health technology appraisal. The text also fully reflects the very latest advances in the health economics field and the key journal literature. Large-scale survey datasets, in particular complex survey designs such as panel data, provide a rich source of information for health economists. They offer the scope to control for individual heterogeneity and to model the dynamics of individual behaviour. However, the measures of outcome used in health economics are often qualitative or categorical. These create special problems for estimating econometric models. The dramatic growth in computing power over recent years has been accompanied by the development of methods that help to solve these problems. The purpose of this book is to provide a practical guide to the skills required to put these techniques into practice. Practical applications of the methods are illustrated using data on health from the British Health and Lifestyle Survey (HALS), the British Household Panel Survey (BHPS), the European Community Household Panel (ECHP), the US Medical Expenditure Panel Survey (MEPS) and Survey of Health, Ageing and Retirement in Europe (SHARE). There is a strong emphasis on applied work, illustrating the use of relevant computer software with code provided for Stata. Familiarity with the basic syntax and structure of Stata is assumed. The Stata code and extracts from the statistical output are embedded directly in the main text and explained at regular intervals. The book is built around empirical case studies, rather than general theory, and the emphasis is on learning by example. It presents a detailed dissection of methods and results of some recent research papers written by the authors and their colleagues. Relevant methods are presented alongside the Stata code that can be used to implement them and the empirical results are discussed at each stage. This text brings together the theory and application of health economics and econometrics, and will be a valuable reference for applied economists and students of health economics and applied econometrics.
Applied Holography: A Practical Mini-Course (SpringerBriefs in Physics)
by Matteo BaggioliThis primer is a collection of notes based on lectures that were originally given at IIT Madras (India) and at IFT Madrid (Spain). It is a concise and pragmatic course on applied holography focusing on the basic analytic and numerical techniques involved. The presented lectures are not intended to provide all the fundamental theoretical background, which can be found in the available literature, but they concentrate on concrete applications of AdS/CFT to hydrodynamics, quantum chromodynamics and condensed matter. The idea is to accompany the reader step by step through the various benchmark examples with a classmate attitude, providing details for the computations and open-source numerical codes in Mathematica, and sharing simple tricks and warnings collected during the author’s research experience. At the end of this path, the reader will be in possess of all the fundamental skills and tools to learn by him/herself more advanced techniques and to produce independent and novel research in the field.
Applied Impulsive Mathematical Models (CMS Books in Mathematics)
by Ivanka Stamova Gani StamovUsing the theory of impulsive differential equations, this book focuses on mathematical models which reflect current research in biology, population dynamics, neural networks and economics. The authors provide the basic background from the fundamental theory and give a systematic exposition of recent results related to the qualitative analysis of impulsive mathematical models. Consisting of six chapters, the book presents many applicable techniques, making them available in a single source easily accessible to researchers interested in mathematical models and their applications. Serving as a valuable reference, this text is addressed to a wide audience of professionals, including mathematicians, applied researchers and practitioners.
Applied Integer Programming
by Der-San Chen Yu Dang Robert G. BatsonAn accessible treatment of the modeling and solution of integer programming problems, featuring modern applications and softwareIn order to fully comprehend the algorithms associated with integer programming, it is important to understand not only how algorithms work, but also why they work. Applied Integer Programming features a unique emphasis on this point, focusing on problem modeling and solution using commercial software. Taking an application-oriented approach, this book addresses the art and science of mathematical modeling related to the mixed integer programming (MIP) framework and discusses the algorithms and associated practices that enable those models to be solved most efficiently.The book begins with coverage of successful applications, systematic modeling procedures, typical model types, transformation of non-MIP models, combinatorial optimization problem models, and automatic preprocessing to obtain a better formulation. Subsequent chapters present algebraic and geometric basic concepts of linear programming theory and network flows needed for understanding integer programming. Finally, the book concludes with classical and modern solution approaches as well as the key components for building an integrated software system capable of solving large-scale integer programming and combinatorial optimization problems.Throughout the book, the authors demonstrate essential concepts through numerous examples and figures. Each new concept or algorithm is accompanied by a numerical example, and, where applicable, graphics are used to draw together diverse problems or approaches into a unified whole. In addition, features of solution approaches found in today's commercial software are identified throughout the book.Thoroughly classroom-tested, Applied Integer Programming is an excellent book for integer programming courses at the upper-undergraduate and graduate levels. It also serves as a well-organized reference for professionals, software developers, and analysts who work in the fields of applied mathematics, computer science, operations research, management science, and engineering and use integer-programming techniques to model and solve real-world optimization problems.
Applied Intelligence: Second International Conference, ICAI 2024, Zhengzhou, China, November 22–25, 2024, Proceedings, Part II (Communications in Computer and Information Science #2388)
by De-Shuang Huang Wei Chen Chuanlei Zhang Yijie Pan Qinhu Zhang Xiangzeng KongThis 2-volume set CCIS 2387 + 2388 constitutes the proceedings of the Second International Conference on Applied Intelligence, ICAI 2024, held in Zhengzhou, China, during November 22-25, 2024. The 72 full papers presented in this proceedings were carefully reviewed and selected from 228 submissions. The papers cover a wide range on theoretical aspects of biomedical data modeling and mining; computer vision; and deep learning. They were organized in topical sections as follows: Part I: Biomedical data modeling and mining; information security; pattern recognition; Part II: Image Processing; intelligent data analysis and prediction; machine learning;
Applied Intelligence: Second International Conference, ICAI 2024, Zhengzhou, China, November 22–25, 2024, Proceedings, Part I (Communications in Computer and Information Science #2387)
by De-Shuang Huang Wei Chen Chuanlei Zhang Yijie Pan Qinhu Zhang Xiangzeng KongThis 2-volume set CCIS 2387 + 2388 constitutes the proceedings of the Second International Conference on Applied Intelligence, ICAI 2024, held in Zhengzhou, China, during November 22-25, 2024. The 72 full papers presented in this proceedings were carefully reviewed and selected from 228 submissions. The papers cover a wide range on theoretical aspects of biomedical data modeling and mining; computer vision; and deep learning. They were organized in topical sections as follows: Part I: Biomedical data modeling and mining; information security; pattern recognition; Part II: Image Processing; intelligent data analysis and prediction; machine learning;
Applied Intelligence: First International Conference, ICAI 2023, Nanning, China, December 8–12, 2023, Proceedings, Part II (Communications in Computer and Information Science #2015)
by De-Shuang Huang Prashan Premaratne Changan YuanThis 2-volume set CCIS 2014 and CCIS 2015 constitutes the post-conference proceedings of the First International Conference on Applied Intelligence, ICAI 2023, held in Nanning, China, December 8–12, 2023.The 64 full papers presented in this proceedings were carefully selected and reviewed from 228 submissions. The papers cover a wide range on theoretical aspects of biomedical data modeling and mining; computer vision; and deep learning. They were organized in topical sections as follows:Part I: Biomedical data modeling and mining; computer vision; deep learning; Part II: Intelligent control and automation; machine learning; natural language processing and computational linguistics.
Applied Inverse Problems: Select Contributions from the First Annual Workshop on Inverse Problems (Springer Proceedings in Mathematics & Statistics #48)
by Larisa BeilinaThis proceedings volume is based on papers presented at the First Annual Workshop on Inverse Problems which was held in June 2011 at the Department of Mathematics, Chalmers University of Technology. The purpose of the workshop was to present new analytical developments and numerical methods for solutions of inverse problems. State-of-the-art and future challenges in solving inverse problems for a broad range of applications was also discussed. The contributions in this volume are reflective of these themes and will be beneficial to researchers in this area.
Applied Iterative Methods (Dover Books on Mathematics)
by Louis A. Hageman David M. YoungThis graduate-level text examines the practical use of iterative methods in solving large, sparse systems of linear algebraic equations and in resolving multidimensional boundary-value problems. Assuming minimal mathematical background, it profiles the relative merits of several general iterative procedures. Topics include polynomial acceleration of basic iterative methods, Chebyshev and conjugate gradient acceleration procedures applicable to partitioning the linear system into a "red/black" block form, adaptive computational algorithms for the successive overrelaxation (SOR) method, and computational aspects in the use of iterative algorithms for solving multidimensional problems. 1981 edition. 48 figures. 35 tables.
Applied Linear Algebra
by Ben Noble James W. DanielContains important modern applications such as signal processing and Karmarkar's approach to linear programming. Uses Gauss reduction and 'Gauss-reduced form' as the fundamental theoretical and computational tool. Includes examples and problems using modern software for matrix computations, and describes properties and sources of software for real applied problems. Stresses both the theoretical and practical importance of tools such as the singular-value decomposition and generalized (pseudo) inverses, the QR decomposition, Householder transformations/matrices, and orthogonal projections. Features 1,100 exercises, including optional computer examples and problems.
Applied Linear Algebra and Matrix Analysis (Undergraduate Texts In Mathematics)
by Thomas S. ShoresIn its second edition, this textbook offers a fresh approach to matrix and linear algebra. Its blend of theory, computational exercises, and analytical writing projects is designed to highlight the interplay between these aspects of an application. This approach places special emphasis on linear algebra as an experimental science that provides tools for solving concrete problems.<P><P> The second edition’s revised text discusses applications of linear algebra like graph theory and network modeling methods used in Google’s PageRank algorithm. Other new materials include modeling examples of diffusive processes, linear programming, image processing, digital signal processing, and Fourier analysis. These topics are woven into the core material of Gaussian elimination and other matrix operations; eigenvalues, eigenvectors, and discrete dynamical systems; and the geometrical aspects of vector spaces.<P> Intended for a one-semester undergraduate course without a strict calculus prerequisite, Applied Linear Algebra and Matrix Analysis augments the key elements of linear algebra with a wide choice of optional sections. With the book’s selection of applications and platform-independent assignments, instructors can tailor the curriculum to suit specific interests and ensure students across various disciplines are equipped with the powerful tools of linear algebra.
Applied Linear Algebra and Matrix Methods (Springer Undergraduate Texts in Mathematics and Technology)
by Timothy G. FeemanThis textbook is designed for a first course in linear algebra for undergraduate students from a wide range of quantitative and data driven fields. By focusing on applications and implementation, students will be prepared to go on to apply the power of linear algebra in their own discipline. With an ever-increasing need to understand and solve real problems, this text aims to provide a growing and diverse group of students with an applied linear algebra toolkit they can use to successfully grapple with the complex world and the challenging problems that lie ahead. Applications such as least squares problems, information retrieval, linear regression, Markov processes, finding connections in networks, and more, are introduced on a small scale as early as possible and then explored in more generality as projects. Additionally, the book draws on the geometry of vectors and matrices as the basis for the mathematics, with the concept of orthogonality taking center stage. Important matrix factorizations as well as the concepts of eigenvalues and eigenvectors emerge organically from the interplay between matrix computations and geometry.The R files are extra and freely available. They include basic code and templates for many of the in-text examples, most of the projects, and solutions to selected exercises. As much as possible, data sets and matrix entries are included in the files, thus reducing the amount of manual data entry required.
Applied Linear Algebra, Probability and Statistics: A Volume in Honour of C. R. Rao and Arbind K. Lal (Indian Statistical Institute Series)
by Ravindra B. Bapat Manjunatha Prasad Karantha Stephen J. Kirkland Samir Kumar Neogy Sukanta Pati Simo PuntanenThis book focuses on research in linear algebra, statistics, matrices, graphs and their applications. Many chapters in the book feature new findings due to applications of matrix and graph methods. The book also discusses rediscoveries of the subject by using new methods. Dedicated to Prof. Calyampudi Radhakrishna Rao (C.R. Rao) who has completed 100 years of legendary life and continues to inspire us all and Prof. Arbind K. Lal who has sadly departed us too early, it has contributions from collaborators, students, colleagues and admirers of Professors Rao and Lal. With many chapters on generalized inverses, matrix analysis, matrices and graphs, applied probability and statistics, and the history of ancient mathematics, this book offers a diverse array of mathematical results, techniques and applications. The book promises to be especially rewarding for readers with an interest in the focus areas of applied linear algebra, probability and statistics.
Applied Linear Models with SAS
by Daniel ZeltermanThis textbook for a second course in basic statistics for undergraduates or first-year graduate students introduces linear regression models and describes other linear models including Poisson regression, logistic regression, proportional hazards regression, and nonparametric regression. Numerous examples drawn from the news and current events with an emphasis on health issues illustrate these concepts. Assuming only a pre-calculus background, the author keeps equations to a minimum and demonstrates all computations using SAS. Most of the programs and output are displayed in a self-contained way, with an emphasis on the interpretation of the output in terms of how it relates to the motivating example. Plenty of exercises conclude every chapter. All of the datasets and SAS programs are available from the book's website, along with other ancillary material.
Applied Linear Regression
by Sanford WeisbergPraise for the Third Edition"...this is an excellent book which could easily be used as a course text..."--International Statistical InstituteThe Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples.Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illustrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. While maintaining the accessible appeal of each previous edition,Applied Linear Regression, Fourth Edition features:Graphical methods stressed in the initial exploratory phase, analysis phase, and summarization phase of an analysisIn-depth coverage of parameter estimates in both simple and complex models, transformations, and regression diagnosticsNewly added material on topics including testing, ANOVA, and variance assumptionsUpdated methodology, such as bootstrapping, cross-validation binomial and Poisson regression, and modern model selection methodsApplied Linear Regression, Fourth Edition is an excellent textbook for upper-undergraduate and graduate-level students, as well as an appropriate reference guide for practitioners and applied statisticians in engineering, business administration, economics, and the social sciences.
Applied Linear Regression for Business Analytics with R: A Practical Guide to Data Science with Case Studies (International Series in Operations Research & Management Science #337)
by Daniel P. McGibneyApplied Linear Regression for Business Analytics with R introduces regression analysis to business students using the R programming language with a focus on illustrating and solving real-time, topical problems. Specifically, this book presents modern and relevant case studies from the business world, along with clear and concise explanations of the theory, intuition, hands-on examples, and the coding required to employ regression modeling. Each chapter includes the mathematical formulation and details of regression analysis and provides in-depth practical analysis using the R programming language.
Applied Linear Regression for Longitudinal Data: With an Emphasis on Missing Observations (Chapman & Hall/CRC Texts in Statistical Science)
by Frans E.S. Tan Shahab JolaniThis book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputation are explained conceptually and consequences of missing observations are clarified using visualization techniques. Key features include the following: • Provides datasets and examples online • Gives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysis • Conceptualises the analysis of comparative (experimental and observational) studies It is the ideal companion for researcher and students in epidemiological, health, and social and behavioral sciences working with longitudinal studies without a mathematical background. Frans E.S. Tan is an associate professor (retired) of methodology and statisticsat Maastricht University, The Netherlands. Shahab Jolani is an assistant professor of methodology and statistics atMaastricht University, The Netherlands.
Applied Logistic Regression (Wiley Series in Probability and Statistics #398)
by Stanley Lemeshow David W. Hosmer Jr. Rodney X. SturdivantA new edition of the definitive guide to logistic regression modeling for health science and other applicationsThis thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include:A chapter on the analysis of correlated outcome dataA wealth of additional material for topics ranging from Bayesian methods to assessing model fitRich data sets from real-world studies that demonstrate each method under discussionDetailed examples and interpretation of the presented results as well as exercises throughoutApplied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.
Applied Longitudinal Analysis
by Garrett M. Fitzmaurice James H. Ware Nan M. LairdFeatures newly developed topics and applications of the analysis of longitudinal data. Applied Longitudinal Analysis, Second Edition presents modern methods for analyzing data from longitudinal studies and now features the latest state-of-the-art techniques. The book emphasizes practical, rather than theoretical, aspects of methods for the analysis of diverse types of longitudinal data that can be applied across various fields of study, from the health and medical sciences to the social and behavioral sciences.The authors incorporate their extensive academic and research experience along with various updates that have been made in response to reader feedback. The Second Edition features six newly added chapters that explore topics currently evolving in the field, including:Fixed effects and mixed effects models. Marginal models and generalized estimating equations. Approximate methods for generalized linear mixed effects models. Multiple imputation and inverse probability weighted methods. Smoothing methods for longitudinal data. Sample size and power. Each chapter presents methods in the setting of applications to data sets drawn from the health sciences. New problem sets have been added to many chapters, and a related website features sample programs and computer output using SAS, Stata, and R, as well as data sets and supplemental slides to facilitate a complete understanding of the material.With its strong emphasis on multidisciplinary applications and the interpretation of results, Applied Longitudinal Analysis, Second Edition is an excellent book for courses on statistics in the health and medical sciences at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for researchers and professionals in the medical, public health, and pharmaceutical fields as well as those in social and behavioral sciences who would like to learn more about analyzing longitudinal data.
Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence
by Judith D. Singer John B. WillettChange is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences. It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models. Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods.