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This book is devoted to the problems of construction and application of chi-squared goodness-of-fit tests for complete and censored data. Classical chi-squared tests assume that unknown distribution parameters are estimated using grouped data, but in practice this assumption is often forgotten. In this book, we consider modified chi-squared tests, which do not suffer from such a drawback. The authors provide examples of chi-squared tests for various distributions widely used in practice, and also consider chi-squared tests for the parametric proportional hazards model and accelerated failure time model, which are widely used in reliability and survival analysis. Particular attention is paid to the choice of grouping intervals and simulations. This book covers recent innovations in the field as well as important results previously only published in Russian. Chi-squared tests are compared with other goodness-of-fit tests (such as the Cramer-von Mises-Smirnov, Anderson-Darling and Zhang tests) in terms of power when testing close competing hypotheses.

The statistical analysis of discrete multivariate data has received a great deal of attention in the statistics literature over the past two decades. The develop­ ment ofappropriate models is the common theme of books such as Cox (1970), Haberman (1974, 1978, 1979), Bishop et al. (1975), Gokhale and Kullback (1978), Upton (1978), Fienberg (1980), Plackett (1981), Agresti (1984), Goodman (1984), and Freeman (1987). The objective of our book differs from those listed above. Rather than concentrating on model building, our intention is to describe and assess the goodness-of-fit statistics used in the model verification part of the inference process. Those books that emphasize model development tend to assume that the model can be tested with one of the traditional goodness-of-fit tests 2 2 (e.g., Pearson's X or the loglikelihood ratio G) using a chi-squared critical value. However, it is well known that this can give a poor approximation in many circumstances. This book provides the reader with a unified analysis of the traditional goodness-of-fit tests, describing their behavior and relative merits as well as introducing some new test statistics. The power-divergence family of statistics (Cressie and Read, 1984) is used to link the traditional test statistics through a single real-valued parameter, and provides a way to consolidate and extend the current fragmented literature. As a by-product of our analysis, a new 2 2 statistic emerges'between'Pearson's X and the loglikelihood ratio G that has some valuable properties.

This concise book for practitioners presents the statistical analysis of operational risk, which is considered the most relevant source of bank risk, after market and credit risk. The book shows that a careful statistical analysis can improve the results of the popular loss distribution approach. The authors identify the risk classes by applying a pooling rule based on statistical tests of goodness-of-fit, use the theory of the mixture of distributions to analyze the loss severities, and apply copula functions for risk class aggregation. Lastly, they assess operational risk data in order to estimate the so-called capital-at-risk that represents the minimum capital requirement that a bank has to hold. The book is primarily intended for quantitative analysts and risk managers, but also appeals to graduate students and researchers interested in bank risks.

Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures.In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics: Advice on selecting prior distributions Computational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures, including sensitivity to priors Frequentist properties of Bayesian methods Case studies covering advanced topics illustrate the flexibility of the Bayesian approach: Semiparametric regression Handling of missing data using predictive distributions Priors for high-dimensional regression models Computational techniques for large datasets Spatial data analysis The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book's website.Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award.Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute.

This text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation analysis as the core issues. Data summarization, both quantitative and categorical, is treated within the encoder-decoder paradigm bringing forward a number of mathematically supported insights into the methods and relations between them. Two Chapters describe methods for categorical summarization: partitioning, divisive clustering and separate cluster finding and another explain the methods for quantitative summarization, Principal Component Analysis and PageRank. Features:· An in-depth presentation of K-means partitioning including a corresponding Pythagorean decomposition of the data scatter. · Advice regarding such issues as clustering of categorical and mixed scale data, similarity and network data, interpretation aids, anomalous clusters, the number of clusters, etc.· Thorough attention to data-driven modelling including a number of mathematically stated relations between statistical and geometrical concepts including those between goodness-of-fit criteria for decision trees and data standardization, similarity and consensus clustering, modularity clustering and uniform partitioning.New edition highlights: · Inclusion of ranking issues such as Google PageRank, linear stratification and tied rankings median, consensus clustering, semi-average clustering, one-cluster clustering· Restructured to make the logics more straightforward and sections self-containedCore Data Analysis: Summarization, Correlation and Visualization is aimed at those who are eager to participate in developing the field as well as appealing to novices and practitioners.

Chapter One addresses the importance of weighted linear regression in fitting straight lines. In Chapter Two, the authors cover the homocedastic condition, i.e. variance of y's independent of x, errors of y's accumulative, the heterocedastic case, i.e. variance or standard deviation proportional to x values, respectively, and orthogonal regression (error in both axes). The chapter also covers topics such as prediction (using the regression line in reverse), leverage, goodness of fit, comparison between models with and without intercept, uncertainty, polynomial regression models without intercept, and an overview of robust regression through the origin. Chapter Three focuses on linear regression for interval-valued data within the framework of random sets, and proposes a new model that generalizes a series of existing ones. Chapter Four provides an investigation on modeling of adsorption of heavy metal ions onto surface-functionalized polymer beads. Linear and non-linear regressions were employed for each of the isotherm models considered to describe the equilibrium data. To reliably assess model validity, various error functions (whose mathematical expressions contain the number of experimental measurements, the numbers of independent variables and parameters in the regression equation as well as the measured and predicted equilibrium adsorption capacities) were used.

Represents a self-contained account of a new promising and generally applicable approach to a large class of one-sided testing problems, where the alternative is restricted by at least two linear inequalities. It highlights the geometrical structure of these problems. It gives guidance in the construction of a so-called Circular Likelihood Ratio (CLR) test, which is obtained if the linear inequalities, or polyhedral cone, are replaced by one suitable angular inequality, or circular cone. Such a test will often constitute a nice and easy-to-use compromise between the LR-test and a suitable linear test against the original alternative. The book treats both theory and practice of CLR-tests. For cases with up to 13 linear inequalities, it evaluates the power of CLR-tests, derives the most stringent CLR-test, and provides tables of critical values. It is of interest both to the specialist in order- restricted inference and to the statistical consultant in need of simple and powerful one-sided tests. Many examples are worked out for ANOVA, goodness-of-fit, and contingency table problems. Case studies are devoted to Mokken's one- dimensional scaling model, one-sided treatment comparison in a two-period crossover trial, and some real data ANOVA- layouts (biology and educational psychology).

This book is written in the hope that it will serve as a companion volume to my first monograph. The first monograph was largely devoted to the probabilistic aspects of the inverse Gaussian law and therefore ignored the statistical issues and related data analyses. Ever since the appearance of the book by Chhikara and Folks, a considerable number of publications in both theory and applications of the inverse Gaussian law have emerged thereby justifying the need for a comprehensive treatment of the issues involved. This book is divided into two sections and fills up the gap updating the material found in the book of Chhikara and Folks. Part I contains seven chapters and covers distribution theory, estimation, significance tests, goodness-of-fit, sequential analysis and compound laws and mixtures. The first part forms the backbone of the theory and wherever possible I have provided illustrative examples for easy assimilation of the theory. The second part is devoted to a wide range of applications from various disciplines. The applied statistician will find numerous instances of examples which pertain to a first passage time situation. It is indeed remarkable that in the fields of life testing, ecology, entomology, health sciences, traffic intensity and management science the inverse Gaussian law plays a dominant role. Real life examples from actuarial science and ecology came to my attention after this project was completed and I found it impossible to include them.

Core Concepts in Data Analysis: Summarization, Correlation and Visualization provides in-depth descriptions of those data analysis approaches that either summarize data (principal component analysis and clustering, including hierarchical and network clustering) or correlate different aspects of data (decision trees, linear rules, neuron networks, and Bayes rule).Boris Mirkin takes an unconventional approach and introduces the concept of multivariate data summarization as a counterpart to conventional machine learning prediction schemes, utilizing techniques from statistics, data analysis, data mining, machine learning, computational intelligence, and information retrieval.Innovations following from his in-depth analysis of the models underlying summarization techniques are introduced, and applied to challenging issues such as the number of clusters, mixed scale data standardization, interpretation of the solutions, as well as relations between seemingly unrelated concepts: goodness-of-fit functions for classification trees and data standardization, spectral clustering and additive clustering, correlation and visualization of contingency data. The mathematical detail is encapsulated in the so-called “formulation” parts, whereas most material is delivered through “presentation” parts that explain the methods by applying them to small real-world data sets; concise “computation” parts inform of the algorithmic and coding issues. Four layers of active learning and self-study exercises are provided: worked examples, case studies, projects and questions.

The theory of empirical processes constitutes the mathematical toolbox of asymptotic statistics. Its growth was accelerated by the 1950s work on the Functional Central Limit Theorem and the Invariance Principle. The theory has developed in parallel with statistical methodologies, and has been successfully applied to a large diversity of problems related to the asymptotic behaviour of statistical procedures. The three sets of lecture notes in the book offer a wide panorama of contemporary empirical processes theory. Techniques are developed in the framework of probability in Banach spaces, Hungarian-style strong approximations, using tools from general stochastic process theory. Other tools appear in this text in connection with historical as well as modern applications, such as goodness-of-fit tests, density estimation or general M-estimators. This book gives an excellent overview of the broad scope of the theory of empirical processes. It will be an invaluable aid for students and researchers interested in an advanced and well-documented approach to the selected topics.

Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions of boundary corrections for trigonometric series estimators; detailed asymptotics for polynomial regression; testing goodness-of-fit; estimation in partially linear models; practical aspects, problems and methods for co

This book will introduce new physical approaches (include fractional derivative models, continuous time random walk methods and Hausdorff derivative models) to accurately characterize anomalous sediment transport in turbulent flow. This book will systematically investigate anomalous sediment transport inexperiments, physical analysis, stochastic model and field applications.

The intricate interplay between food, energy, water, societal dynamics, and the environment constitutes the bedrock of contemporary global challenges. In recent decades, this convergence has emerged as a focal point for scientific inquiry, policy formulation, and societal discourse. This book seeks to meticulously unravel the complexities inherent in this nexus, shedding profound light on the interconnected risks, vulnerabilities, and transformative opportunities that define our quest for sustainability in the twenty-first century. The materials of this book were prepared mainly by authors working on the joint project between the International Institute for Applied Systems Analysis (IIASA) and the National Academy of Sciences (NAS) of Ukraine “Integrated robust modeling and management of food-energy-water-land use nexus for sustainable development”, and on the project “Comprehensive analysis of robust preventive and adaptive measures of food, energy, water, and social management in the context of systemic risks and consequences of COVID-19” (0122U000552) of the NAS of Ukraine. Members of the System Analysis Committee of the NAS of Ukraine were also involved in the preparation of book chapters. The authors dedicate this book to the memory of the Academician of NAS of Ukraine, Professor Yuri Ermoliev, who died in October 2022 and made a fundamental contribution to stochastic optimization as a basic tool for quantitative risk management. This book largely follows his ideas about risk management and its applications.

This book addresses the latest developments in Information Technologies (IT) for geo-engineering. It contains papers from the 5th International Conference on Information Technology in Geo-engineering (5th ICITG), held in Golden, Colorado, USA, 2024. The book is divided into the following chapters: 1) Geotechnical Instrumentation, Sensor and Sensing Technology; 2) Imaging Technology, Virtual and Augmented Reality, Information and Computer Technology; 3) Data-Driven Investigation and Modeling, Big Data and Databases; and 4) Machine Learning and Artificial Intelligence. IT encompasses a broad field with extensive and powerful uses in geo-engineering, particularly in dealing with large amounts of uncertain data typical of many geo-engineering research and projects. Given its broad range and most up-to-date coverage, the book benefits students, educators, researchers, and professional practitioners, encouraging them to take the geo-engineering community into the digital age.

The Proceedings of the Fifth International Conference on Emerging Trends in Mathematical Sciences & Computing (IEMSC-24) contains papers that present the current scientific as well as technological innovations by leading academicians, eminent researchers, and experts throughout the globe in the twin domain of Mathematical Sciences as well as Computing. The papers focus on the recent advances in the field of Theoretical Computer Science as well as its blending with Mathematical techniques. The book aims to disseminate new technical ideas and features that can be incorporated in day-to-day life for the benefit of the society. The research papers exhibit scientific advancements in diversified spectrum that includes Differential as well as Integral Equations with applications, Computational Fluid Dynamics, Nanofluids, Network Theory & Optimization, Control Theory, Machine Learning & Artificial Intelligence, Big Data Analytics, IoT, Cryptography, Fuzzy Automata, Statistics, and many more. The proceedings primarily focus on the amalgamation of mathematical methods with computing. The potential readers will get access to diverse ideas and innovations in the field of computing together with its growing interactions in various fields of mathematics. This book serves as a valuable reference resource for researchers in academia and industry.

This second edition of the book analyses the latest developments in investment management policy for institutional investors. It is divided into four parts, which have been updated to include the newest approaches and strategies in asset allocation, portfolio management and performance evaluation. The first part analyses the different types of institutional investors, i.e. institutions that professionally manage portfolios of financial and real assets on behalf of a wide range of individuals. This part goes on with an in-depth analysis of the economic, technical and regulatory characteristics of the various categories of investment funds and of other types of asset management products, which have a high rate of substitutability with investment funds and represent their natural competitors. The second part of the book identifies and investigates the stages of the investment portfolio management. Given the importance of strategic asset allocation in explaining the ex post performance of any type of investment portfolio, this part provides an in-depth analysis of asset allocation methods, illustrating the different theoretical and operational solutions available to institutional investors. The third part describes performance assessment, its breakdown and risk control, with an in-depth examination of performance evaluation techniques, returns-based style analysis approaches and performance attribution models. Finally, the fourth part deals with the subject of diversification into alternative asset classes, identifying the common characteristics and their possible role within the framework of investment management policy. This part analyses hedge funds, private equity, private debt, real estate, infrastructures, commodities and currency overlay techniques.

This book presents a unique collection of contributions on modern methods and applications in three key areas of statistics, celebrating the significant work of Wolfgang Schmid in this field. It is structured thematically into parts focusing on statistical process monitoring, financial statistics, and spatial statistics with environmetrics, each featuring chapters from leading experts. The opening articles on statistical process monitoring present novel methodologies for the detection of anomalies and control charting techniques, which are crucial for maintaining quality in manufacturing processes. Detailed discussions are included on integrating multivariate statistical methods and real-time monitoring to enhance process reliability and efficiency. The part on financial statistics explores rigorous approaches in financial econometrics, with an emphasis on dynamic modelling of market volatility and risk assessment. Contributions cover advanced asset allocation strategies, leveraging high-dimensional data analysis, and the application of machine learning techniques. Spatial statistics and environmetrics are addressed through innovative research on the statistical analysis of environmental data. This includes the use of geostatistical models and hybrid models that combine traditional statistical techniques with machine learning to improve the prediction of environmental phenomena. Key topics here involve the modelling of extremes and airborne pollutants, the prediction of earthquakes using a smartphone-based sensor network, and reviews of selected topics essential in modern spatial statistics. Each part not only reflects Wolfgang Schmid's interests and impact in these areas but also provides detailed theoretical and applied studies, demonstrating how these sophisticated statistical methods can be effectively employed in practical scenarios. This makes the book an indispensable resource for researchers and practitioners looking to apply cutting-edge statistical techniques in these complex fields.

In a world expected to reach a population of almost 10 billion inhabitants by 2050 and facing rapid global warming, it is essential to develop studies to better characterize and preserve the existing environmental heritage. Among these assets, soil stands out, which is fundamental for the balance of life on the planet (air quality and composition, temperature regulation, carbon and nutrient cycling, water cycling and quality, natural'waste'(decomposition) treatment and recycling, and habitat for most living things and their food). Due its importance, Soil information is increasing recently in order to attend human demands for land use management and capability, reduce erosion risks and soil security, for example. Particularly in Brazil, advances in soil science are now required to attend the new national systematic soil survey that have as a goal soil sustainability and security. Gathering machine learning tools, pedometrics and pedological concepts is a way to achieve these demands regarding soil data and related products. This book summarizes remarkable insights from the II Pedometrics Brazil Conference providing cutting-edge information to researchers, students and professionals working with soils in tropical countries such as Brazil.

Founded in 1985 by Jean-Claude Falmagne and Jean-Paul Doignon, Knowledge Structure Theory (KST) constitutes a rigorous and current mathematical theory for the representation and the assessment of human knowledge. The seminal work of these authors initiated a highly active research strand with an ever-growing literature, mostly scattered across various technical journals.Starting from a concise but comprehensive introduction to its foundations, this volume provides a state-of-the-art review of KST. For the first time the volume brings together the most important theoretical developments and extensions of the last decade and presents new areas of application beyond education, with contributions by key researchers in the field.Among the important advances covered by this book are (1) a comprehensive treatment of probabilistic models in KST; (2) polytomous extensions of the theory; (3) KST-based psychological diagnostics and neuropsychological assessment; (4) the representation and assessment of cognitive skills in problem solving, as well as procedural skills. In addition, this book also includes an overview of available software for the application of KST.

This book collects selected, peer-reviewed research presented at the 8th International Conference on Mathematics: Pure, Applied, and Computation, held in Lombok, Indonesia, on 30 September 2023. Organised into three parts—Part I: Control Systems, Mathematical Simulation and Modeling; Part II: Formal Methods and Data Science; Part III: Graph Theory and Analysis—the book contains 29 peer-reviewed chapters. Ranging from theoretical to applied results, the book addresses the mathematical models for several phenomena such as investment behavior, unmanned surface vehicles and electronic medical records. It also highlights the progress in the use of satisfiability methods and tools to solve puzzle and pencil games. It showcases how mathematics is used to solve real-world problems.

Numerical methods are the mathematical procedures that approximate the solution of complex mathematical problems into much simpler form and which find a wide variety of use while solving complex Physical Chemistry problems. This book aims to aide in understanding of such numerical methods including solving complex differential equations and numerical differentiation & integration. Moreover it also explains various statistical tests used in Analytical Chemistry for data analysis. The author has tried to include as many example from Chemistry problems for a better understanding of the methods.

This proceedings volume gathers selected, peer-reviewed papers presented at the Dynamical Systems Theory and Applications International Conference - DSTA 2021, held virtually on December 6-9, 2021, organized by the Department of Automation, Biomechanics, and Mechatronics at Lodz University of Technology, Poland. This volume concentrates on studies on applications, while Volume II focuses on numerical and analytical approaches.Being a truly international conference, this 16th iteration of DSTA received submissions from authors representing 52 countries. The program covered both theoretical and experimental approaches to widely understood dynamical systems, including topics devoted to bifurcations and chaos, control in dynamical systems, asymptotic methods in nonlinear dynamics, stability of dynamical systems, lumped mass and continuous systems vibrations, original numerical methods of vibration analysis, nonsmooth systems, dynamics in life sciences and bioengineering, as well as engineering systems and differential equations.DSTA conferences aim to provide a common platform for exchanging new ideas and results of recent research in scientific and technological advances in modern dynamical systems. Works contained in this volume can appeal to researchers in the field, whether in mathematics or applied sciences, and practitioners in myriad industries.

In an increasingly globally-connected world, the ability to predict, monitor, and contain pandemics is essential to ensure the health and well-being of all. This contributed volume investigates several mathematical techniques for the modeling and simulation of viral pandemics, with a special focus on COVID-19. Modeling a pandemic requires an interdisciplinary approach with other fields such as epidemiology, virology, immunology, and biology in general. Spatial dynamics and interactions are also important features to be considered, and a multiscale framework is needed at the societal level, the level of individuals, and the level of virus particles and the immune system. Chapters in this volume explore the latest research related to these items to demonstrate the utility of a variety of mathematical methods. Perspectives for the future are also offered

Continuing the author's previous work on modeling, this book presents the most recent advances in high-order predictive modeling. The author begins with the mathematical framework of the 2nd-BERRU-PM methodology, an acronym that designates the “second-order best-estimate with reduced uncertainties (2nd-BERRU) predictive modeling (PM).” The 2nd-BERRU-PM methodology is fundamentally anchored in physics-based principles stemming from thermodynamics (maximum entropy principle) and information theory, being formulated in the most inclusive possible phase-space, namely the combined phase-space of computed and measured parameters and responses.The 2nd-BERRU-PM methodology provides second-order output (means and variances) but can incorporate, as input, arbitrarily high-order sensitivities of responses with respect to model parameters, as well as arbitrarily high-order moments of the initial distribution of uncertain model parameters, in order to predict best-estimate mean values for the model responses (i.e., results of interest) and calibrated model parameters, along with reduced predicted variances and covariances for these predicted responses and parameters.

This book is a comprehensive guide to understanding the potential of artificial intelligence (AI) in improving business functions, as well as the limitations and challenges that come with its implementation. In this book, readers will learn about the various opportunities that AI presents in business, including how it can automate routine tasks, reduce errors, and increase efficiency. The book covers a range of topics, including how AI can be used in financial reporting, auditing, fraud detection, and tax preparation. However, the book also explores the limitations of AI in business, such as the need for skilled professionals, data quality, and the potential for bias. It examines the challenges that companies face when implementing AI in business functions, including the need for ethical considerations, transparency, and accountability. The book is written for business professionals, business leaders, and anyone interested in the potential of AI in business functions. It offers practical advice on how to implement AI effectively and provides insights into the latest developments in AI technology. Through case studies and real-world examples, readers will gain a deeper understanding of how AI can be used to enhance business functions, as well as the potential pitfalls and limitations to be aware of. Overall, this book is an essential guide for anyone looking to harness the power of AI to improve their business functions and to stay ahead in an increasingly competitive business environment.

This book is a collection of selected research papers presented at the Mathematics, Statistics and Computing Technology (ICMSCT2023), held at the UST Angelicum College, Philippines, from 20th to 21st September 2023. This biennial event is a result from collaborations of university partners in Malaysia, Thailand, Indonesia and Philippines. Increasing investment in digital technologies is a challenge faced by most countries after the crisis caused by COVID-19 and the demand of technological revolution 4.0. Indirectly, regardless of their level of development, they take into account the importance of redesigning strategies for resilient and sustainable regional economic development, increasing regional resilience and minimizing recovery costs as a basis for development. In such situation, this book gather discussion, viewpoints and findings on the recent works of mathematical and computing technology applications in order to propose solutions to overcome adversity of digital resilience. This book covers a wide range of topics on applied mathematics, which includes decision mathematics and also applied statistics covering statistical learning with applications. In addition, the book also highlight the latest application of statistical mining and data visualization, particularly on data mining, machine learning and data visualization. Editors believe this book will interest and influence researchers on the recent techniques, methodologies and applications to ensure digital resilience and support future research.

The book'Problems in Mathematical Biophysics - a volume in memory of Alberto Gandolfi'aims at reviewing the current state of the art of the mathematical approach to various areas of theoretical biophysics. Leading authors in the field have been invited to contribute, having a strong appreciation of Alberto Gandolfi as a scientist and as a man and sharing his same passion for biology and medicine, as well as his style of investigation. Encompassing both theoretical and practical aspects of Mathematical Biophysics, the topics covered in this book span a spectrum of different problems, in biology, and medicine, including population dynamics, tumor growth and control, immunology, epidemiology, ecology, and others. As a result, the book offers a comprehensive and current overview of compelling subjects and challenges within the realm of mathematical biophysics. In their contributions, the authors have effectively conveyed not only their research findings but also their peculiar perspective and approach to problem-solving, dealing with oncology, epidemiology, neuro-sciences, and biochemistry. The chapters pertain to a wide array of mathematical areas such as continuous Markov chains, partial differential equations, kinetic theory, applied statistical mechanics, noise-induced transitions, and many others.

In an era defined by technological breakthroughs such as AI, blockchain, and IoT, this book offers a fresh and practical approach to Business Model Innovation (BMI). It delves into how technological advancements drive new business models and enhance operational efficiency, providing actionable insights and real-world examples for business leaders, strategists, operations managers, entrepreneurs, and students in business and technology disciplines. Encouraging diverse research methods, including theoretical, empirical, and multimethod studies, it welcomes manuscripts with clear managerial or policy implications. Aimed at students, scholars, researchers, professionals, executives, government agencies, and policymakers, this book equips readers with tools to succeed in today's dynamic business environment and supports multidisciplinary research to advance innovation management practices.

In an era defined by technological breakthroughs such as AI, blockchain, and IoT, this book offers a fresh and practical approach to Business Model Innovation (BMI). It delves into how technological advancements drive new business models and enhance operational efficiency, providing actionable insights and real-world examples for business leaders, strategists, operations managers, entrepreneurs, and students in business and technology disciplines. Encouraging diverse research methods, including theoretical, empirical, and multimethod studies, it welcomes manuscripts with clear managerial or policy implications. Aimed at students, scholars, researchers, professionals, executives, government agencies, and policymakers, this book equips readers with tools to succeed in today's dynamic business environment and supports multidisciplinary research to advance innovation management practices.

This book shows how modern risk management in banks and insurance companies can be modeled in Excel and Matlab. Readers are provided with all the necessary knowledge and skills in a systematic and structured step-by-step manner. Apart from basic Excel knowledge, no previous knowledge is required. The textbook is divided into five parts. First, the reader learns the basics of analyzing and modeling market risks. Next, the authors introduce and explain the modeling of credit risks and operational risks are quantified by calibrating loss distributions based on expert estimates. Furthermore, individual risk measures are examined in more detail. In order to calculate a risk measure for an overall portfolio to determine the risk capital, the question of the aggregation method is discussed. There are various common concepts for this, which are examined in more detail in the last part of the book.The book is aimed at students of business administration with a focus on financial services. Accompanying the book, readers receive Excel spreadsheets as digital bonus material for practice and application.

This volume collects the proceedings of the International Conference on Recent Developments in Mathematics (ICRDM), held at Canadian University Dubai, UAE, in August 2022. This is the second of two volumes, with this volume focusing on more applied topics, particularly mathematical modeling and scientific computing, and the first covering recent advances in algebra and analysis. Each chapter identifies existing research problems, the techniques needed to solve them, and a thorough analysis of the obtained results. Advances in Mathematical Modeling and Scientific Computing will appeal to a range of postgraduate students, researchers, and industry professionals interested in exploring recent advancements in applied mathematics.

This book provides a self-contained introduction to a new class of stochastic models for systems of spiking neurons. These systems have a large number of interacting components, each one evolving as a stochastic process with a memory of variable length. Several mathematical tools are put to use, such as Markov chains, stochastic chains having memory of variable length, point processes having stochastic intensity, Hawkes processes, random graphs, mean field limits, perfect sampling algorithms, the Context algorithm, and statistical model selection.The book's focus on mathematically tractable objects distinguishes it from other texts on theoretical neuroscience. The biological complexity of neurons is not ignored, but reduced to some of its main features, such as the intrinsic randomness of neuronal dynamics. This reduction in complexity aims at explaining and reproducing statistical regularities and collective phenomena that are observed in experimental data, an approach that leads to mathematically rigorous results. With an emphasis on a constructive and algorithmic point of view, this book is directed towards mathematicians interested in learning about stochastic network models and their neurobiological underpinning, and neuroscientists interested in learning how to build and prove results with mathematical models that relate to actual experimental settings.

This book is a comprehensive compilation of articles that delve into the forefront of interdisciplinary applications of innovative technologies. It presents the scientific inquiries and outcomes showcased at the 15th Days of the Bosnian-Herzegovinian American Academy of Arts and Sciences conference, held in Sarajevo, Bosnia and Herzegovina, from June 20 to 23, 2024. The collection highlights the latest advancements and will draw the interest of researchers in diverse domains of engineering, including civil engineering, data science and geographic information systems, computer science and artificial intelligence, advanced environmental engineering and project management, information and communication technologies, and advanced electrical power systems. This book serves as a testament to the ongoing pursuit of knowledge and innovation in these fields, offering insights into the current research landscape and future directions. The contributions not only expand the theoretical foundations but also explore practical applications that address contemporary challenges in technology and engineering. The editors gratefully acknowledge the dedicated efforts of all the symposia chairs of the 15th Days of BHAAAS whose meticulous planning and scholarly oversight have enriched this book and contributed to its scholarly significance.

This three-volume book gathers peer-reviewed papers presented at the 21st International Conference on Geometry and Graphics (ICGG 2024), held in Kitakyushu, Japan, from 5 to 9 August 2024. The conference started in 1978 and is promoted by the International Society for Geometry and Graphics, which aims to foster international collaboration and stimulate the scientific research and teaching methodology in the fields of Geometry and Graphics. The ICGG 2024 covered the following five topics taken over from ICGG 2022: Theoretical Graphics and Geometry; Applied Geometry and Graphics; Engineering Computer Graphics; Graphics Education; Geometry and Graphics in History, to which a new section of Related Topics was added in response to the growing body of research on Geometry and Graphics. Volume 2 contains papers on Applied Geometry and Graphics among these topics. Given its breadth of coverage, the book will introduce engineers, architects, and designers interested in computer applications, graphics, and geometry to the latest advances in the field, with a particular focus on science, the arts, and mathematics education.

This book presents a unique historical study on the origins of longevity risk management and its links to religious institutions in the eighteenth century. Throughout history, monarchs, affluent patrons, and wealthy benefactors routinely pledged to provide their devotees with pensions or life annuities, mirroring the biblical concept of ‘daily bread for life'. Until the eighteenth century, however, the uncertainty around the longevity of beneficiaries'lives and the difficulty of budgeting for random financial obligations had posed economic challenges that often led to insufficient funding and high default rates. This book narrates the genesis of longevity risk pooling and the first successfully funded annuity scheme in history, an eventual prototype for national pension plans around the world. It examines how a group of Protestant clergymen, scientists, and intellectuals associated with the Presbyterian Church of Scotland pioneered innovative methods for setting up a reversionary annuity and widow's pension plan, guided by actuarial principles. Unknown to many, the economist Adam Smith, and other literati of the Scottish Enlightenment, invested in this novel annuity. Illuminating the social and theological contexts of this scheme, the book argues that religious belief played a critical role in the development of best practices around the prudent management of longevity risk. The practices, values and beliefs in divine probabilities were at the heart of these thought leaders'confidence in long-term financial projections. Shedding light on this fascinating aspect of actuarial history by an examination of the archival records, while also linking to contextual discussions of modern pension challenges, this book will be of interest to scholars and readers interested in finance, insurance, pensions, and religion.

This book is written for statisticians, data analysts, programmers, researchers, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using R object-oriented programming language and RStudio integrated development environment (IDE). R is an open-source software with a development environment (RStudio) for computing statistics and graphical displays through data manipulation, modeling, and calculation. R packages and supported libraries provide a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical software, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system. Therefore, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the users. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and nonparametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for the reliability and validity of the available datasets. Different research experiments, case scenarios, and examples are explained in this book. The book provides a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations thus congruence of Statistics and Computer programming in Research.

This comprehensive book explores the transformative role of artificial intelligence (AI) in business innovation and research. It provides a solid foundation in AI technologies, such as machine learning, natural language processing, and computer vision, and examines how they reshape business models and revolutionize industries. The book highlights the strategic implications of AI in enhancing customer experience, optimizing operations, and enabling data-driven decision-making. It explores the integration of AI with emerging trends like IoT, blockchain, and cloud computing for innovation. The role of AI in advancing scientific discovery and academic research is also explored, addressing challenges and opportunities in AI-driven methodologies. Organizational and ethical dimensions of AI implementation are considered, including talent acquisition, skills development, and data governance. Real-world case studies showcase AI's transformative power across diverse industries. This forward-thinking guide equips academics, researchers, and business leaders with knowledge and insights to harness the potential of AI and contribute to innovation and research.

This book presents the proceedings of the 5th International Conference on Reliability Safety & Hazard-2024, held in Mumbai during February 21–24, 2024. It covers the latest advances in artificial intelligence and machine learning in development of risk-conscious culture. Various topics covered in this volume are reliability prediction, precursor event analysis, fuzzy reliability, structural reliability, passive system reliability, digital system reliability, risk-informed approach to decision making, dynamic PSA, uncertainty and sensitivity modeling, among others. The book is a valuable resource for researchers and professionals working in both academia and industry in the areas of complex systems, safety-critical systems, and risk-based engineering.

This volume gathers selected papers presented at the ICMSQUARE 2023 - 12th International Conference on Mathematical Modeling in Physical Sciences held in Belgrade, Serbia from August 28–31, 2023. This proceedings offers a compilation of cutting-edge research, which aims to advance the knowledge and development of high-quality research in mathematical fields related to physics, chemistry, biology, medicine, economics, environmental sciences, and more. Annually held since 2012, the ICMSQUARE conference serves as a platform for the exchange of ideas and discussions on the latest technological trends in these fields. This book is an invaluable resource for researchers, academicians, and professionals in these areas seeking to stay up-to-date with the latest developments in mathematical modeling.

This book is a comprehensive guide to understanding the potential of artificial intelligence (AI) in improving business functions, as well as the limitations and challenges that come with its implementation. In this book, readers will learn about the various opportunities that AI presents in business, including how it can automate routine tasks, reduce errors, and increase efficiency. The book covers a range of topics, including how AI can be used in financial reporting, auditing, fraud detection, and tax preparation. However, the book also explores the limitations of AI in business, such as the need for skilled professionals, data quality, and the potential for bias. It examines the challenges that companies face when implementing AI in business functions, including the need for ethical considerations, transparency, and accountability. The book is written for business professionals, business leaders, and anyone interested in the potential of AI in business functions. It offers practical advice on how to implement AI effectively and provides insights into the latest developments in AI technology. Through case studies and real-world examples, readers will gain a deeper understanding of how AI can be used to enhance business functions, as well as the potential pitfalls and limitations to be aware of. Overall,'AI in Business: Opportunities and Limitations'is an essential guide for anyone looking to harness the power of AI to improve their business functions, and to stay ahead in an increasingly competitive business environment.

This textbook introduces quantitative methods in operations management, based on operational research. Written for undergraduate and graduate students as well as practitioners, this book serves as a valuable compendium of essential tools for project planning, control, and strategic decision-making.Drawing from the expertise of both experienced scientists and seasoned practical managers, the descriptions of each tool are a harmonious blend of theoretical insights and real-world applicability. With a focus on accessibility, the authors have thoughtfully combined abstract concepts with easy-to-follow examples and detailed case studies.Readers will benefit from the abundance of well-explained recommendations and practical problem-solving approaches, where the book offers guidance on how to solve presented issues by using commercial software. Whether one seeks to refine project management, optimize operations, or make strategic choices, this book equips readers with theknowledge and proficiency required to excel in the dynamic field of operations management.

Presently, we stand on the threshold of a technological revolution that will drastically change the way we live, work, and communicate with each other. By the current rate, scope, and complexity, this transformation will be as fundamental for society as any other technological paradigm change from the past. The industries which are more susceptible to change are technologically oriented industries including banking, finance, accounting, and auditing. One of the technological concepts of the technological revolution is the concept of the digital twin.The application of digital twins and AI as paired with Internet of Things technologies makes it possible to solve ESG problems on a completely different level (Li, 2019) for accounting firms and financial institutions. These include recycling on demand, rational energy consumption, smart surveillance cameras for crime tracking, and smart branch parking solutions, monitoring the wear and tear and conditions of financial technology infrastructures.Moreover, numerous researchers and practitioners emphasize the significance of innovating sustainable business models and operations (Geissdoerfer et al., 2018). The digital twin will allow businesses and financial institutions to minimize costs, boost customer service, and find new ways to generate revenue. DTW is accessible now more than ever, and many reputable and innovative companies such as Tesla, Ericsson, and Siemens have adopted it with varying success.Therefore, this book examines the opportunities, challenges, and risks of artificial intelligence-augmented digital twins for financial operations, innovation, and sustainable development. It focuses on AI and digital twin technologies to furnish solutions for the current industrial revolution including the Metaverse. Henceforth, this book aims to encourage authors to submit multi-disciplinary chapters indicating the current scholarly challenges about the applications and potential of artificial intelligence and digital twins in accounting, finance, and banking.

Handbook of Whale Optimization Algorithm: Variants, Hybrids, Improvements, and Applications provides the most in-depth look at an emerging meta-heuristic that has been widely used in both science and industry. Whale Optimization Algorithm has been cited more than 5000 times in Google Scholar, thus solving optimization problems using this algorithm requires addressing a number of challenges including multiple objectives, constraints, binary decision variables, large-scale search space, dynamic objective function, and noisy parameters to name a few. This handbook provides readers with in-depth analysis of this algorithm and existing methods in the literature to cope with such challenges. The authors and editors also propose several improvements, variants and hybrids of this algorithm. Several applications are also covered to demonstrate the applicability of methods in this book. Provides in-depth analysis of equations, mathematical models and mechanisms of the Whale Optimization Algorithm Proposes different variants of the Whale Optimization Algorithm to solve binary, multiobjective, noisy, dynamic and combinatorial optimization problems Demonstrates how to design, develop and test different hybrids of Whale Optimization Algorithm Introduces several application areas of the Whale Optimization Algorithm, focusing on sustainability Includes source code from applications and algorithms that is available online

The two volume set CCIS 2032 and 2033 constitutes the proceedings of the 11th Congress on Simulation for a Sustainable Future, EUROSIM 2023, which was held in Amsterdam, The Netherlands, during July 3–5, 2023. The 47 full papers included in the proceedings were carefully reviewed and selected from 99 submissions. The papers are divided in the following topical sections: ​environmental sustainability; healthcare; production systems; business and industries; logistics and transportation systems; monitor, control, and theoretical systems.

This book presents the latest breakthroughs and cutting-edge advancements within this rapidly evolving field. By providing computational finance and business analytics, organizations can secure a competitive advantage in today's data-driven and cutting-edge business landscape. This book explores the most recent innovations and significant developments in both the domains of computational finance and business analytics, offering a thorough overview of the current landscape. It encompasses various dimensions including: Business Analytics Financial Analytics HR & Marketing Analytics By integrating the latest theoretical insights with practical applications, this book equips researchers, practitioners, and students with the knowledge and tools necessary to explore and progress in the ever-changing realm of computational finance and business analytics. As the present organizations confront the challenges and adapt the opportunities presented by the data revolution, this book serves as an essential guide, illuminating the transformative frontiers where computational finance and business analytics are redefining the realm of possibilities.

This book is the proceedings of the 4th International Conference on Advances in Computational Science and Engineering (ICACSE 2023, December 16–17, 2023, Manila, Philippines) and contains the selected peer-reviewed papers which reflect recent achievements in the field of application of the computational methods and algorithms in scientific research and engineering design. The papers presented covered topics such as advances in system integration, high-performance computing, modeling, and simulation, big data analytics, big data visualization, advanced networking and applications, cybersecurity, augmented and virtual reality, artificial intelligence and robotics, soft computing data science, and intelligent knowledge discovery. The book is useful, interesting, and informative for a wide range of scientists, engineers, and students.

The purpose of this work is to focus mathematical modeling on issues affecting women's health. Working in six collaborative teams, researchers developed new mathematical models to address questions in a range of application areas. Topics include HIV, oral contraceptives, blood clotting, breast cancer, neonatal respiration, and outbreak forecasting. The work is the result of newly formed collaborative groups begun during the Collaborative Workshop for Women in Mathematical Biology hosted by the Institute of Mathematics and its Applications and UnitedHealth Group Optum in June 2022. This is an open access book.

This book presents peer reviewed articles from the International Conference on Fundamental and Industrial Research on Materials- iConFIRM 2023; held from 11th to 14th Dec at Ropar in India. It includes recent advances in the area of mechanics of metallic, nano and energy materials, extractive metallurgy, and processing. Fundamental research works including development and characterization of new alloys, ceramics, composites and nano materials along with advanced characterization techniques such as XRD, SEM and TEM and mathematical modelling, finite element simulations, molecular dynamics, machine learning and similar other advanced numerical, theoretical and experimental techniques in the field of materials and metallurgy.

This thoroughly revised second edition Handbook provides an authoritative and in-depth overview of choice modelling, a key technique used across disciplines as diverse as transport, marketing, health and environmental economics.Composed of contributions from influential senior researchers, this erudite Handbook covers all the significant steps of choice modelling analysis, including underlying economic and psychological theory, data collection and sampling, model specification and estimation, and interpretation and use of results. New chapters examining topics including endogeneity in discrete choice models, machine learning, and novel data sources such as virtual reality provide a fresh outlook on this fundamental empirical methodology. This second edition of the Handbook of Choice Modelling will be an important read for academics and students across disciplines that have an interest in behavioural modelling. It will also benefit practitioners seeking to understand the theoretical underpinning of their work.

In the last decades, machine learning techniques – especially techniques of deep learning – led to numerous successes in many application areas, including economics. The use of machine learning in economics is the main focus of this book; however, the book also describes the use of more traditional econometric techniques. Applications include practically all major sectors of economics: agriculture, health (including the impact of Covid-19), manufacturing, trade, transportation, etc. Several papers analyze the effect of age, education, and gender on economy – and, more generally, issues of fairness and discrimination.We hope that this volume will:help practitioners to become better knowledgeable of the state-of-the-art econometric techniques, especially techniques of machine learning,and help researchers to further develop these important research directions. We want to thank all the authors for their contributions and all anonymous referees for their thorough analysis and helpful comments.