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In a VUCA world, which is becoming increasingly volatile, uncertain, and complex, companies, organizations, and states must respond promptly and adequately to the respective situations. Making decisions based on past experiences is less successful in these times than having an accurate understanding of current conditions. The importance of empirical sciences, continuous environmental observation, timely analysis of causal relationships, and deriving new insights from them is increasing. From this, it can be deduced which measures are likely to achieve one's goals with predictable probability, such as which price for an offer generates the desired demand or which marketing measure reaches the desired target group. Where classical statistics were once used for calculations and predictions, today free (open source) tools like R allow data in various formats and from any number of sources to be read in, processed, and analyzed using methods of Artificial Intelligence and Machine Learning. The results can then be perfectly visualized so that decision-makers can benefit quickly and effectively. The age of Data Science has arrived. Digitalization is more than a buzzword or a promise; it is actionable and usable for everyone. This book teaches you, based on the latest version of R at the time of publication, how to use Artificial Intelligence and Machine Learning in Industry 4.0.

This textbook presents the fundamental economic dimensions of insurance companies and links them to managerial issues. Combining academic rigour and a strongly practice-oriented approach, it addresses both the competitive environment and the management of the insurance business. Further, it provides a general overview of insurance undertakings and technical topics are explained in depth. Filling an important gap in the market for textbooks on the insurance business, it is divided into four parts and 35 chapters. Part I (chapters 1 to 10) describes the fundamentals of the business, how the industry works, the Authorities and the regulations. It presents the insurance products (for life, non-life retail, and non-life commercial lines). Part II (chapters 11 to 17) explains the pricing and reserving for life and non-life insurance. Reinsurance business is also illustrated. Part III (chapters 18 to 25) describes business models in the industry and the organizational structures. The main processes of an insurance company (product development, underwriting, claims settlement, investments) are presented. Marketing and distribution are also described. Part IV (chapters 26 to 35) defines the financial statement and introduces IFRS principles. Solvency II calculation, ALM model, and Embedded Value are explained in detail. This part also describes management accounting, performance indicators, and the Business Plan in the insurance industry. The book offers a valuable resource for lower and upper undergraduate students, graduate students, professionals/practitioners working at insurance companies, insurance agents, brokers, bankers, and consultants.

This book explores multiple types of innovation within the modern wine industry and how it has developed historically. The book provides and extensive examination of the existing knowledge in this subject. This highlights the evolution of historical and contemporary trends and signposts the authoritative literature published and most important researchers active in the field. With this comprehensive approach, the book is a versatile resource for both scholars and industry professionals, akin to a'Swiss Army Knife'for all aspects of green innovation in the wine sector.

This book provides an introductory overview of random variables and their transformations. The authors approach the topic with statistics students in mind, along with researchers in various fields who are interested in data analysis. The book begins with by defining and explaining mathematical expectation. The authors then discuss transformations of random variables, including distribution functions and special functions. The book also covers joint probability distribution and its applications. The authors have updated and expanded upon their writing on these topics, which they originally covered in their previous book, Statistics for Scientists and Engineers.

This book presents the econometric foundations and applications of multi-dimensional panels, including modern methods of big data analysis. In light of the big data revolution and the emergence of higher dimensional panel data sets, it provides new results to synthesize existing knowledge on the field. The first, theoretical part of the volume is providing the econometric foundations to deal with these new high-dimensional panel data sets. It not only synthesizes our current knowledge, but mostly, presents new research results. The second empirical part of the book provides insight into the most relevant applications in this area. These chapters are a mixture of surveys and new results, always focusing on the econometric problems and feasible solutions.This second extended and revised edition provides an update of all existent chapters to reflect on new developments in the area as well as several new chapters on topics such as machine learning, nonparametric models,networks, and multi-dimensional panels in health economics. The book serves as a standard reference work, a textbook for graduate students in economics, and a source of background material for professionals conducting empirical studies.

This book focuses on the Frobenius theorem regarding a nonlinear simultaneous system. The Frobenius theorem is well known as a condition for a linear simultaneous system's having a nonnegative solution. Generally, however, the condition of a simultaneous system, including a non-linear system's having a nonnegative solution, is hardly discussed at all. This book, therefore, extends the conventional Frobenius theorem for nonlinear simultaneous systems for economic analysis. Almost all static optimization problems in economics involve nonlinear programing. Theoretical models in economics are described in the form of a simultaneous system resulting from the rational optimization behavior of households and enterprises. On the other hand, rational optimization behavior of households and enterprises is, mathematically speaking, expressed as nonlinear programing. For this reason it is important to understand the meaning of nonlinear programing. Because this book includes explanations of the relations among various restrictions in a nonlinear programing systematically and clearly, this book is suitable for students in graduate school programs in economics.

This book offers a thorough understanding of Hierarchical Archimedean Copulas (HACs) and their practical applications. It covers the basics of copulas, explores the Archimedean family, and delves into the specifics of HACs, including their fundamental properties. The text also addresses sampling algorithms, HAC parameter estimation, and structure, and highlights temporal models with applications in finance and economics. The final chapter introduces R, MATLAB, and Octave toolboxes for copula modeling, enabling students, researchers, data scientists, and practitioners to model complex dependence structures and make well-informed decisions across various domains.

This book provides professionals and practitioners with a comprehensive guide on credit risk modeling, capital modeling, and validation for Current Expected Credit Loss (CECL), International Financial Reporting Standard 9 (IFRS9), Basel Capital and Comprehensive Capital Analysis and Review (CCAR) procedures. It describes how credit risk modeling, capital modeling, and validation are done in big banks with code and examples. The book features innovative concepts such as Binary Logit Approximation (BLA) for Competing Risk Framework; Adaptive and Exhaustive Variable Selection (AEVS) for automatic modeling; Full Observation Stratified Sampling (FOSS) for unbiased sampling; and Prohibited Correlation Index (PCI) for Fair Lending Texts. It also features a chapter on credit underwriting and scoring, addressing the credit underwriting risk with some innovations. It is a valuable guide for professionals, practitioners and graduate students in risk management.

This book focuses on four key issues that are crucial to achieving productivity and sustainability in the circular supply chain management industry. These key topics are the elements of circular supply chain management, statistical techniques such as the design of experiments and statistical process control, well-known fuzzy techniques in the performance measurement analysis of circular supply chains, and the indicators of managing productivity and sustainability before and after implementing the model. Presenting a creative combination of fuzzy and statistical techniques on performance measurement in circular supply chain management, this book would be of interest to experts and technical managers who are responsible for implementing productivity and sustainability models within the organization as well as academics.

This textbook teaches the basics of econometrics and focuses on the acquisition of methods and skills that are essential for any student to succeed in their studies, as well as for any practitioner interested in applying econometric techniques. Employing a pedagogical and easy-to-follow style, the book puts into practice the various concepts presented, such as statistics, tests, and methods, among others. Numerous examples and empirical applications using existing econometric and statistical software are given after each theoretical presentation.The book addresses students at the undergraduate and graduate levels in economics and management, as well as students of engineering and business schools. It will further appeal to professionals and practitioners of econometrics, such as economists and researchers in companies and institutions, who will find practical solutions to the different problems they are confronted with.

Basisboek Statistiek met Excel In Basisboek Statistiek met Excel vind je aanwijzingen voor het verwerken en analyseren van onderzoeksgegevens met behulp van Excel. Met dit boek raak je als student in het hoger onderwijs al doende vertrouwd met de statistische procedures in Excel, en ook met statistiek. Aan de hand van een databestand over de relatie tussen geld, geluk en gezondheid ga je concreet aan de slag. Om verantwoord statistische procedures met Excel te kunnen uitvoeren is niet alleen kennis van Excel, maar ook statistische kennis nodig. Daarom komen diverse statistische basisbegrippen aan bod en technieken die je gebruikt om gegevens te beschrijven, verschillen te toetsen en om samenhang na te gaan. De auteurs leggen zo veel mogelijk in woorden en met voorbeelden de essentie uit van iedere techniek, zonder daar diep wiskundig op in te gaan.

Adam Grant's “8 New Idea Books to Start Spring”Next Big Idea Club's Must-Read Books for May 2024'A wonderful litany of the myriad ways in which we can be deceived, and deceive ourselves.'—The Guardian“Highlights how first recognizing our biases and then taking small but intentional steps to overcome them can have an outsized effect on the quality of our decisions.'—Inc.How our biases cause us to fall for misinformation—and how to combat it. Our lives are minefields of misinformation. It ripples through our social media feeds, our daily headlines, and the pronouncements of politicians, executives, and authors. Stories, statistics, and studies are everywhere, allowing people to find evidence to support whatever position they want. Many of these sources are flawed, yet by playing on our emotions and preying on our biases, they can gain widespread acceptance, warp our views, and distort our decisions. In this eye-opening book, renowned economist Alex Edmans teaches us how to separate fact from fiction. Using colorful examples—from a wellness guru's tragic but fabricated backstory to the blunders that led to the Deepwater Horizon disaster to the diet that ensnared millions yet hastened its founder's death—Edmans highlights the biases that cause us to mistake statements for facts, facts for data, data for evidence, and evidence for proof. Armed with the knowledge of what to guard against, he then provides a practical guide to combat this tide of misinformation. Going beyond simply checking the facts and explaining individual statistics, Edmans explores the relationships between statistics—the science of cause and effect—ultimately training us to think smarter, sharper, and more critically. May Contain Lies is an essential read for anyone who wants to make better sense of the world and better decisions.

The book presents a collection of peer-reviewed short papers selected from those presented at the International Conference Mathematical and Statistical Methods for Actuarial Sciences and Finance – MAF2024. The year 2024 marks the twentieth anniversary of the first edition of this conference. The idea behind this book is that collaboration and cross-pollination between mathematicians and statisticians working in actuarial sciences and finance could enhance research in these fields. The effectiveness of this concept has been demonstrated by widespread participation across all editions, held at various locations including the University of Salerno, Italy (2004, 2006, 2010, 2014, and 2022); Ca'Foscari University of Venice, Italy (2008, 2012, and 2020); University Paris-Dauphine in Paris, France (2016); University Carlos III of Madrid, Madrid (2018); and University of Le Havre Normandie, Le Havre, France (2024). This effectiveness is also evident in the attention consistently shown by both the scientific community and professionals toward the volumes of peer-reviewed papers accompanying all past editions of MAF. The book is a valuable resource for academics, researchers, Ph.D. students, and professionals. Furthermore, it is also of interest to other readers with a quantitative background.

Written by an experienced statistics educator and two data scientists, this book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning. It provides an accessible approach for readers with a basic statistics background to develop a mastery of machine learning. The book starts with elucidating examples in Chapter 1 and fundamentals on refined optimization in Chapter 2, which are followed by common supervised learning methods such as regressions, classification, support vector machines, tree algorithms, and range regressions. After a discussion on unsupervised learning methods, it includes a chapter on unsupervised learning and a chapter on statistical learning with data sequentially or simultaneously from multiple resources.One of the distinct features of this book is the comprehensive coverage of the topics in statistical learning and medical applications. It summarizes the authors'teaching, research, and consulting experience in which they use data analytics. The illustrating examples and accompanying materials heavily emphasize understanding on data analysis, producing accurate interpretations, and discovering hidden assumptions associated with various methods.Key Features: Unifies conventional model-based framework and contemporary data-driven methods into a single overarching umbrella over data science. Includes real-life medical applications in hypertension, stroke, diabetes, thrombolysis, aspirin efficacy. Integrates statistical theory with machine learning algorithms. Includes potential methodological developments in data science.

This book is the latest title of the popular Excel textbook; redesigned, while including interactive, user-friendly JMP to encourage business students to develop competitive advantages for use in their future careers. How can performance outcome drivers be identified? How can performance outcomes be forecast? Use of regression, conjoint analysis, Monte Carlo simulation provide answers and solutions for specific scenarios. Students learn to build models, produce statistics, and translate results into implications for decision makers.The text features new and updated examples and assignments, and each chapter discusses a focal case from the business world which can be analyzed using the statistical strategies and software provided in the text. Paralleling recent interest in climate change and sustainability, new case studies concentrate on issues such as the impact of drought on business, automobile emissions, and sustainable package goods.The book continues its coverage of inference, Monte Carlo simulation, contingency analysis, and linear and nonlinear regression. A new chapter is dedicated to conjoint analysis design and analysis, including complementary use of regression and JMP.

Dieses Buch führt in die ökonometrische Analyse von Querschnitts-, Zeitreihen- und Paneldaten unter Anwendung statistischer Software ein. Es dient als Grundlagentext für diejenigen, die ökonometrische Analysen in der empirischen Forschung erlernen und anwenden wollen. Das Niveau der Darstellung ist so einfach wie möglich gehalten, um es sowohl für Studenten im Grundstudium als auch für Doktoranden nützlich zu machen. Es enthält mehrere Beispiele mit realen Daten und Stata-Programmen sowie die Interpretation der Ergebnisse. Bei der Diskussion der statistischen Instrumente, die zum Verständnis der empirischen Wirtschaftsforschung erforderlich sind, versucht das Buch, ein Gleichgewicht zwischen Theorie und angewandter Forschung herzustellen. Verschiedene Konzepte und Techniken der ökonometrischen Analyse werden durch sorgfältig entwickelte Beispiele unter Verwendung des statistischen Softwarepakets Stata 15.1 unterstützt, wobei vorausgesetzt wird, dass der Leser mit der Software Strataeinigermaßen vertraut ist.

This book delves into contemporary business analytics techniques across sectors for critical decision-making. It combines data, mathematical and statistical models, and information technology to present alternatives for decision evaluation. Offering systematic mechanisms, it explores business contexts, factors, and relationships to foster competitiveness. Beyond managerial perspectives, it includes contributions from professionals, academics, and scholars worldwide, delivering comprehensive knowledge and skills through diverse viewpoints, cases, and applications of analytical tools. As an international business science reference, it targets professionals, academics, researchers, doctoral scholars, postgraduate students, and research organizations seeking a nuanced understanding of modern business analytics.

This volume provides a comprehensive survey that covers various modern methods used for detecting and estimating change points in time series and their models. The book primarily focuses on asymptotic theory and practical applications of change point analysis. The methods discussed in the book go beyond the traditional change point methods for univariate and multivariate series. It also explores techniques for handling heteroscedastic series, high-dimensional series, and functional data. While the primary emphasis is on retrospective change point analysis, the book also presents sequential'on-line'methods for detecting change points in real-time scenarios. Each chapter in the book includes multiple data examples that illustrate the practical application of the developed results. These examples cover diverse fields such as economics, finance, environmental studies, and health data analysis. To reinforce the understanding of the material, each chapter concludes with several exercises.Additionally, the book provides a discussion of background literature, allowing readers to explore further resources for in-depth knowledge on specific topics. Overall,'Change Point Analysis for Time Series'offers a broad and informative overview of modern methods in change point analysis, making it a valuable resource for researchers, practitioners, and students interested in analyzing and modeling time series data.

The authors of this book assert that Grid Square statistics, a method of aggregating data within a geographically defined Grid, may be an effective solution to approach geospatial data for big data integration. Grid Square statistics is a technique that allows us to collect and analyze data based on Grids and makes it easier to understand patterns and trends. Sustainability, a key concern for the future of our society, often involves balancing multiple independent objectives. These objectives and key performance indicators must be shared to solve multi-dimensional optimization problems with some constraints related to sustainability issues. However, many of these sustainability issues are challenges that are part of global systems, which are modeled as a complex system consisting of many components. Social, economic, and environmental aspects in our socio-economic systems require solutions designed for the sustainability of our society. These conditions imply that Big Data must contribute to evaluating our current situation, actions, and changes to continuously change our behavior and society based on a Plan-Do-Check-Action (PDCA) cycle. The PDCA cycle, also known as the Deming cycle, is a four-step management method used to improve processes and products continuously. We must understand how to construct a platform by using Big Data to do so. Thus, extracting meaningful information from Big Data is crucially important in the advanced information society. This book provides case studies based on Grid Square statistics. It shows their applications to socio-economic and environmental problems such as tourism, where we analyze visitor patterns to optimize resource allocation, SDGs indicator, where we monitor the sustainability of our socio-economic systems, and disaster management, where we face natural disaster to enhance preparedness. Finally, the book proposes a World Grid Square Statistics Reference Architecture for constructing data applications across organizations and domains and reports on design concepts, system architecture, and the implication of a data platform for World Grid Square data and statistics.

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.

This book explores the application of complex variables to econometric modeling. Providing a thorough introduction to the theory of complex numbers, it extends these concepts to develop complex-valued models that enhance the accuracy and depth of economic forecasting and data analysis. From simple to multiple complex linear regression, the monograph discusses model formulation, estimation techniques, and correlation analysis, supported by examples in R. This comprehensive guide is a useful resource for students, researchers, and practitioners aiming to apply advanced mathematical techniques to tackle complex real-life problems, making it a useful tool for enhancing predictive analytics in business, economics, and finance.

Make some headway in the notoriously tough subject of business statistics Business Statistics For Dummies helps you understand the core concepts and principles of business statistics, and how they relate to the business world. This book tracks to a typical introductory course offered at the undergraduate, so you know you'll find all the content you need to pass your class and get your degree. You'll get an introduction to statistical problems and processes common to the world of global business and economics. Written in clear and simple language, Business Statistics For Dummies gives you an introduction to probability, sampling techniques and distributions, and drawing conclusions from data. You'll also discover how to use charts and graphs to visualize the most important properties of a data set. Grasp the core concepts, principles, and methods of business statistics Learn tricky concepts with simplified explanations and illustrative graphs See how statistics applies in the real world, thanks to concrete examples Read charts and graphs for a better understanding of how businesses operateBusiness Statistics For Dummies is a lifesaver for students studying business at the college level. This guide is also useful for business professionals looking for a desk reference on this complicated topic.

This book addresses the challenge of presenting biostatistics to medical and health science audiences coherently. Tailored for students and researchers, its 13 chapters progress logically from foundational concepts like measurement scales and statistical calculations to advanced topics such as probability, correlation, regression and health and disease measures. Practical examples enhance relevance, and its gradual approach ensures easy comprehension even for non-statisticians. The book's practical emphasis shines as it culminates in teaching the use of SPSS software for result interpretation, bridging theory and practice effectively. It empowers medical professionals to confidently understand and apply statistical concepts in their work, serving as an indispensable resource in navigating the intricacies of biostatistics in medical and health sciences.

This 3rd edition revised and extended compendium contains and explains essential mathematical formulas within an economic context. Newly added content introduces the mathematical practical application of optimization by using the Lagrange function, presenting the issue of linear optimization cases where the relative extremes (minima or maxima) of a linear (target) function can be determined under restrictive linear constraint and its relevance for business management practice. A broad range of aids and supportive examples will help readers to understand the formulas and their practical applications. This mathematical formulary is presented in a practice-oriented, clear, and understandable manner, as it is needed for meaningful and relevant application in global business, as well as in the academic setting and economic practice. The topics presented include but are not limited to mathematical signs and symbols, logic, arithmetic, algebra, linear algebra, combinatorics, and financial mathematics, including an international comparison between different national methods used in the calculation of interest, optimization of linear models, functions, differential calculus, integral calculus, elasticities, annuity calculation, economic functions, and the Peren Theorem.Given its scope, the book offers an indispensable reference guide and is a must-read for undergraduate and graduate students, as well as managers, scholars, and lecturers in business, politics, and economics.

Data can be extremely valuable if we are able to extract information from them. This is why multivariate data analysis is essential for business and science. This book offers an easy-to-understand introduction to the most relevant methods of multivariate data analysis. It is strictly application-oriented, requires little knowledge of mathematics and statistics, demonstrates the procedures with numerical examples and illustrates each method via a case study solved with IBM's statistical software package SPSS. Extensions of the methods and links to other procedures are discussed and recommendations for application are given. An introductory chapter presents the basic ideas of the multivariate methods covered in the book and refreshes statistical basics which are relevant to all methods.For the 2nd edition, all chapters were checked and calculated using the current version of IBM SPSS.ContentsIntroduction to empirical data analysisRegression analysisAnalysis of varianceDiscriminant analysisLogistic regression Contingency analysisFactor analysisCluster analysisConjoint analysisThe original German version is now available in its 17th edition. In 2015, this book was honored by the Federal Association of German Market and Social Researchers as “the textbook that has shaped market research and practice in German-speaking countries”. A Chinese version is available in its 3rd edition.On the website www.multivariate-methods.info, the authors further analyze the data with Excel and R and provide additional material to facilitate the understanding of the different multivariate methods. In addition, interactive flashcards are available to the reader for reviewing selected focal points. Download the Springer Nature Flashcards App and use exclusive content to test your knowledge.

This book presents the latest developments in the theory and applications of time series analysis and forecasting. Comprising a selection of refereed papers, it is divided into several parts that address modern theoretical aspects of time series analysis, forecasting and prediction, with applications to various disciplines, including econometrics and energy research. The broad range of topics discussed, including matters of particular relevance for sustainable development, will give readers a modern perspective on the subject.The included contributions were originally presented at the 8th International Conference on Time Series and Forecasting, ITISE 2022, held in Gran Canaria, Spain, June 27-30, 2022. The ITISE conference series provides a forum for scientists, engineers, educators and students to discuss the latest advances and implementations in the foundations, theory, models and applications of time series analysis and forecasting. It focuses on interdisciplinary research encompassing computer science, mathematics, statistics and econometrics.

This book provides an accessible introduction to causal inference and data analysis with R, specifically for a public policy audience. It aims to demystify these topics by presenting them through practical policy examples from a range of disciplines. It provides a hands-on approach to working with data in R using the popular tidyverse package. High quality R packages for specific causal inference techniques like ggdag, Matching, rdrobust, dosearch etc. are used in the book.The book is in two parts. The first part begins with a detailed narrative about John Snow's heroic investigations into the cause of cholera. The chapters that follow cover basic elements of R, regression, and an introduction to causality using the potential outcomes framework and causal graphs. The second part covers specific causal inference methods, including experiments, matching, panel data, difference-in-differences, regression discontinuity design, instrumental variables and meta-analysis, with the help of empirical case studies of policy issues. The book adopts a layered approach that makes it accessible and intuitive, using helpful concepts, applications, simulation, and data graphs. Many public policy questions are inherently causal, such as the effect of a policy on a particular outcome. Hence, the book would not only be of interest to students in public policy and executive education, but also to anyone interested in analysing data for application to public policy.

This book provides an extensive, systematic overview of the modern theory of telegraph processes and their multidimensional counterparts, together with numerous fruitful applications in financial modelling. Focusing on stochastic processes of bounded variation instead of classical diffusion, or more generally, Lévy processes, has two obvious benefits. First, the mathematical technique is much simpler, which helps to concentrate on the key problems of stochastic analysis and applications, including financial market modelling. Second, this approach overcomes some shortcomings of the (parabolic) nature of classical diffusions that contradict physical intuition, such as infinite propagation velocity and infinite total variation of paths. In this second edition, some sections of the previous text are included without any changes, while most others have been expanded and significantly revised. These are supplemented by predominantly new results concerning piecewise linear processes with arbitrary sequences of velocities, jump amplitudes, and switching intensities. The chapter on functionals of the telegraph process has been significantly expanded by adding sections on exponential functionals, telegraph meanders and running extrema, the times of the first passages of telegraph processes with alternating random jumps, and distribution of the Euclidean distance between two independent telegraph processes. A new chapter on the multidimensional counterparts of the telegraph processes is also included. The book is intended for graduate students in mathematics, probability, statistics and quantitative finance, and for researchers working at academic institutions, in industry and engineering. It can also be used by university lecturers and professionals in various applied areas.

This monograph presents a theory for random field models in time and space, viewed as stochastic processes with values in a Hilbert space, to model the stochastic dynamics of forward and futures prices in energy, power, and commodity markets. In this book, the well-known Heath–Jarrow–Morton approach from interest rate theory is adopted and extended into an infinite-dimensional framework, allowing for flexible modeling of price stochasticity across time and along the term structure curve. Various models are introduced based on stochastic partial differential equations with infinite-dimensional Lévy processes as noise drivers, emphasizing random fields described by low-dimensional parametric covariance functions instead of classical high-dimensional factor models. The Filipović space, a separable Hilbert space of Sobolev type, is found to be a convenient state space for the dynamics of forward and futures term structures. The monograph provides a classification of important operators in this space, covering covariance operators and the stochastic modeling of volatility term structures, including the Samuelson effect. Fourier methods are employed to price many derivatives of interest in energy, power, and commodity markets, and sensitivity'delta'expressions can be derived. Additionally, the monograph covers forward curve smoothing, the connection between forwards with fixed delivery and delivery period, as well as the classical theory of forward and futures pricing. This monograph will appeal to researchers and graduate students interested in mathematical finance and stochastic analysis applied in the challenging markets of energy, power, and commodities. Practitioners seeking sophisticated yet flexible and analytically tractable risk models will also find it valuable.

Dieses Buch vermittelt Wissen über die statistischen und mathematischen Methoden in der Wirtschaft und beinhaltet die Themenschwerpunkte Analyse, Schätzung und Vorhersage. In jedem Kapitel gibt es ein breites Spektrum an Forschungsarbeiten, die einen Einblick über das Thema geben sowie Beispiele für die Anwendung der Ökonometrie in realen Situationen. Das Buch enthält einen systematischen Literaturüberblick auf der Grundlage maschinell generierter Inhalte. Fragen und zugehörige Schlüsselwörter wurden für die Maschine vorbereitet, die sie abgefragt, entdeckt, zusammengestellt und durch Clustering mit künstlicher Intelligenz (KI) strukturiert hat. Springer Nature hat in den letzten Jahren viel zu dem Thema in Zeitschriften veröffentlicht, so dass die Herausforderung für die Maschine darin bestand, die relevantesten Inhalte zu identifizieren und sie in einer strukturierten Weise zu präsentieren. Die automatisch generierten Literaturzusammenfassungen dienen Wissenschaftlernund Studenten, um sich einen Überblick über die aktuelle Entwicklung des Themas zu verschaffen und Forschungsfragen zu entwickeln, aber auch Personen aus der Praxis erhalten Anregungen und Impulse.

The odds-on best way to master stats. Statistics All-in-One For Dummies is packed with lessons, examples, and practice problems to help you slay your stats course. Develop confidence and understanding in statistics with easy-to-understand (even fun) explanations of key concepts. Plus, you'll get access to online chapter quizzes and other resources that will turn you into a stats master. This book teaches you how to interpret graphs, determine probability, critique data, and so much more. Written by an expert author and serious statistics nerd, Statistics AIO For Dummies explains everything in terms anyone can understand. Get a grasp of basic statistics concepts required in every statistics course Clear up the process of interpreting graphs, understanding polls, and analyzing data Master correlation, regression, and other data analysis tools Score higher on stats tests and get a better grade in your high school or college classStatistics All-in-One For Dummies follows the curriculum of intro college statistics courses (including AP Stats!) so you can learn everything you need to know to get the grade you need—the Dummies way.

Revised and updated for the second edition, this textbook allows students to work through classic texts in economics and finance, using the original data and replicating their results. In this book, the author rejects the theorem-proof approach as much as possible, and emphasizes the practical application of econometrics. They show with examples how to calculate and interpret the numerical results.This book begins with students estimating simple univariate models, in a step by step fashion, using the popular Stata software system. Students then test for stationarity, while replicating the actual results from hugely influential papers such as those by Granger & Newbold, and Nelson & Plosser. Readers will learn about structural breaks by replicating papers by Perron, and Zivot & Andrews. They then turn to models of conditional volatility, replicating papers by Bollerslev. Students estimate multi-equation models such as vector autoregressions and vector error-correction mechanisms, replicating the results in influential papers by Sims and Granger. Finally, students estimate static and dynamic panel data models, replicating papers by Thompson, and Arellano & Bond.The book contains many worked-out examples, and many data-driven exercises. While intended primarily for graduate students and advanced undergraduates, practitioners will also find the book useful.“How to best start learning time series econometrics? Learning by doing. This is the ethos of this book. What makes this book useful is that it provides numerous worked out examples along with basic concepts. It is a fresh, no-nonsense, practical approach that students will love when they start learning time series econometrics. I recommend this book strongly as a study guide for students who look for hands-on learning experience.'--Professor Sokbae'Simon'Lee, Columbia University, Co-Editor of Econometric Theory and Associate Editor of Econometrics Journal.

This book is a practical guide on the use of various data analytics and visualization techniques and tools in the banking and financial sectors. It focuses on how combining expertise from interdisciplinary areas, such as machine learning and business analytics, can bring forward a shared vision on the benefits of data science from the research point of view to the evaluation of policies. It highlights how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the banking and finance.It includes several case studies where innovative data science models is used to analyse, test or model some crucial phenomena in banking and finance. At the same time, the book is making an appeal for a further adoption of these novel applications in the field of economics and finance so that they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies.The book is for stakeholders involved in research and innovation in the banking and financial sectors, but also those in the fields of computing, IT and managerial information systems, helping through this new theory to better specify the new opportunities and challenges. The many real cases addressed in this book also provide a detailed guide allowing the reader to realize the latest methodological discoveries and the use of the different Machine Learning approaches (supervised, unsupervised, reinforcement, deep, etc.) and to learn how to use and evaluate performance of new data science tools and frameworks

This book connects predictive analytics and simulation analytics, with the end goal of providing Rich Information to stakeholders in complex systems to direct data-driven decisions. Readers will explore methods for extracting information from data, work with simple and complex systems, and meld multiple forms of analytics for a more nuanced understanding of data science. The methods can be readily applied to business problems such as demand measurement and forecasting, predictive modeling, pricing analytics including elasticity estimation, customer satisfaction assessment, market research, new product development, and more. The book includes Python examples in Jupyter notebooks, available at the book's affiliated Github.This volume is intended for current and aspiring business data analysts, data scientists, and market research professionals, in both the private and public sectors.

This textbook provides a comprehensive introduction to mathematical calculus. Written for advanced undergraduate and graduate students, it teaches the fundamental mathematical concepts, methods and tools required for various areas of economics and the social sciences, such as optimization and measure theory. The reader will be introduced to topological, metric and normed spaces, learning about numerical sequences, series, and differential and integral calculus. These concepts are introduced using the axiomatic approach as a tool for logical reasoning, consistency, and formalization of ideas. The book follows a theorem-proving approach, stressing the limitations of applying the different theorems, while providing thought-provoking counter-examples. Each chapter features exercises that facilitate learning and allow students to apply and test important concepts and tools.

Für empirische Wirtschaftswissenschaftler gehören ökonometrische Methoden zum Standardwerkzeug. Die neuen Instrumente des maschinellen Lernens setzen sich langsam auch in der Volks- und Betriebswirtschaftslehre durch. Das Buch vermittelt Basiswissen zu den spezifischen Methoden des überwachten und unüberwachten Lernens sowie des Verstärkungslernens. Dabei werden die wesentlichen Unterschiede in Bezug auf Ziele, Methoden und Rahmenbedingungen zwischen den Methoden der Ökonometrie und des maschinellen Lernens dargestellt und erörtert.

Seasonality in economic time series can'obscure'movements of other components in a series that are operationally more important for economic and econometric analyses. In practice, one often prefers to work with seasonally adjusted data to assess the current state of the economy and its future course. This book presents a seasonal adjustment program called CAMPLET, an acronym of its tuning parameters, which consists of a simple adaptive procedure to extract the seasonal and the non-seasonal component from an observed series. Once this process is carried out, there will be no need to revise these components at a later stage when new observations become available. The authors describe the main features of CAMPLET, evaluate the outcomes of CAMPLET and X-13ARIMA-SEATS in a controlled simulation framework using a variety of data generating processes, and illustrate CAMPLET and X-13ARIMA-SEATS with three time series: US non-farm payroll employment, operational income of Ahold and real GDP in the Netherlands. Furthermore they show how CAMPLET performs under the COVID-19 crisis, and its attractiveness in dealing with daily data. This book appeals to scholars and students of econometrics and statistics, interested in the application of statistical methods for empirical economic modeling.

This book presents a selection of peer-reviewed contributions on the latest developments in time series analysis and forecasting, presented at the 7th International Conference on Time Series and Forecasting, ITISE 2021, held in Gran Canaria, Spain, July 19-21, 2021. It is divided into four parts. The first part addresses general modern methods and theoretical aspects of time series analysis and forecasting, while the remaining three parts focus on forecasting methods in econometrics, time series forecasting and prediction, and numerous other real-world applications. Covering a broad range of topics, the book will give readers a modern perspective on the subject.The ITISE conference series provides a forum for scientists, engineers, educators and students to discuss the latest advances and implementations in the foundations, theory, models and applications of time series analysis and forecasting. It focuses on interdisciplinary research encompassing computer science, mathematics,statistics and econometrics.

This book is about data analytics, including problem definition, data preparation, and data analysis. A variety of techniques (e.g., regression, logistic regression, cluster analysis, neural nets, decision trees, and others) are covered with conceptual background as well as demonstrations of KNIME using each tool. The book uses KNIME, which is a comprehensive, open-source software tool for analytics that does not require coding but instead uses an intuitive drag-and-drop workflow to create a network of connected nodes on an interactive canvas. KNIME workflows provide graphic representations of each step taken in analyses, making the analyses self-documenting. The graphical documentation makes it easy to reproduce analyses, as well as to communicate methods and results to others. Integration with R is also available in KNIME, and several examples using R nodes in a KNIME workflow are demonstrated for special functions and tools not explicitly included in KNIME.

This book prepares students to execute the quantitative and computational needs of the finance industry. The quantitative methods are explained in detail with examples from real financial problems like option pricing, risk management, portfolio selection, etc. Codes are provided in R programming language to execute the methods. Tables and figures, often with real data, illustrate the codes. References to related work are intended to aid the reader to pursue areas of specific interest in further detail. The comprehensive background with economic, statistical, mathematical, and computational theory strengthens the understanding. The coverage is broad, and linkages between different sections are explained. The primary audience is graduate students, while it should also be accessible to advanced undergraduates. Practitioners working in the finance industry will also benefit.

THE GUIDE FOR ANYONE AFRAID TO LEARN STATISTICS & ANALYTICS UPDATED WITH NEW EXAMPLES & EXERCISES This book discusses statistics and analytics using plain language and avoiding mathematical jargon. If you thought you couldn't learn these data analysis subjects because they were too technical or too mathematical, this book is for you! This edition delivers more everyday examples and end-of-chapter exercises and contains updated instructions for using Microsoft Excel. You'll use downloadable data sets and spreadsheet solutions, template-based solutions you can put right to work. Using this book, you will understand the important concepts of statistics and analytics, including learning the basic vocabulary of these subjects. Create tabular and visual summaries and learn to avoid common charting errors Gain experience working with common descriptive statistics measures including the mean, median, and mode; and standard deviation and variance, among others Understand the probability concepts that underlie inferential statistics Learn how to apply hypothesis tests, using Z, t, chi-square, ANOVA, and other techniques Develop skills using regression analysis, the most commonly-used Inferential statistical method Explore results produced by predictive analytics software Choose the right statistical or analytic techniques for any data analysis task Optionally, read the “Equation Blackboards,” designed for readers who want to learn about the mathematical foundations of selected methods

The Statistical Yearbook is an annual compilation of a wide range of international economic, social and environmental statistics on over 200 countries and areas, compiled from sources including UN agencies and other international, national and specialized organizations. The 2022 edition contains data available to the Statistics Division as of 31 July 2022 and presents them in 33 tables on topics such as: communication; crime; development assistance; education; energy; environment; finance; gender; international merchandise trade; international tourism; labour force; national accounts; population and migration; price and production indices; and science and technology. Most tables covering the period up to 2022. Accompanying the tables are technical notes providing brief descriptions of major statistical concepts, definitions and classifications.

This volume provides readers with a compact, stimulating and multifaceted introduction to interpretability, a key issue for developing insightful statistical and machine learning approaches as well as for communicating modelling results in business and industry.Different views in the context of Industry 4.0 are offered in connection with the concepts of explainability of machine learning tools, generalizability of model outputs and sensitivity analysis. Moreover, the book explores the integration of Artificial Intelligence and robust analysis of variance for big data mining and monitoring in Additive Manufacturing, and sheds new light on interpretability via random forests and flexible generalized additive models together with related software resources and real-world examples.

In bewährter Weise werden in diesem Lehrbuch grundlegende Begriffe und Verfahren der Statistik erläutert und können anhand von Aufgaben erprobt werden. Das Buch umfasst die Bereiche deskriptive Statistik, Wahrscheinlichkeitsrechnung und induktive Statistik sowie weitere wichtige Teilgebiete der Statistik, wie etwa Prognoserechnung, Ökonometrie, multivariate Verfahren, statistische Entscheidungstheorie und statistische Software. Für die Arbeit mit dem Buch sind mathematische Vorkenntnisse, die üblicherweise im ersten Studienabschnitt in wirtschafts- und sozialwissenschaftlichen Studiengängen vermittelt werden, ausreichend.

This book presents advances in business computing and data analytics by discussing recent and innovative machine learning methods that have been designed to support decision-making processes. These methods form the theoretical foundations of intelligent management systems, which allows for companies to understand the market environment, to improve the analysis of customer needs, to propose creative personalization of contents, and to design more effective business strategies, products, and services. This book gives an overview of recent methods – such as blockchain, big data, artificial intelligence, and cloud computing – so readers can rapidly explore them and their applications to solve common business challenges. The book aims to empower readers to leverage and develop creative supervised and unsupervised methods to solve business decision-making problems.

This book collects select contributions presented at the International Conference on Importance of Statistics in Global Emerging (ISGES 2020) held at the Department of Mathematics and Statistics, University of Pune, Maharashtra, India, from 2–4 January 2020. It discusses recent developments in several areas of statistics with applications of a wide range of key topics, including small area estimation techniques, Bayesian models for small areas, ranked set sampling, fuzzy supply chain, probabilistic supply chain models, dynamic Gaussian process models, grey relational analysis and multi-item inventory models, and more. The possible use of other models, including generalized Lindley shared frailty models, Benktander Gibrat risk model, decision-consistent randomization method for SMART designs and different reliability models are also discussed. This book includes detailed worked examples and case studies that illustrate the applications of recently developed statistical methods, making it a valuable resource for applied statisticians, students, research project leaders and practitioners from various marginal disciplines and interdisciplinary research.

This book explores Complex System Governance (CSG)—an emerging field concerned with the design, execution, and evolution of essential functions necessary to ensure continued viability of a system. The book focuses on three primary development areas to better understand and utilize current developments CSG. First, the conceptual foundations for CSG are developed, from systems theory, management cybernetics, and governance. Second, a set of critical CSG topics are examined from conceptual as well as practice perspectives. Third, several development and application issues are discussed. Ultimately, CSG is positioned as an emerging field with strong theoretical grounding and significant implications for improving practices and performance to better address complex systems and their problems.

This book presents new computation schemes for the sensitivity of options using the binomial tree and introduces readers to the discrete Malliavin calculus. It also shows that applications of the discrete Malliavin calculus approach to the binomial tree model offer fundamental tools for computing Greeks.The binomial tree approach is one of the most popular methods in option pricing. Although it is a fairly traditional model for option pricing, it is still widely used in financial institutions since it is tractable and easy to understand. However, the book shows that the tree approach also offers a powerful tool for deriving the Greeks for options. Greeks are quantities that represent the sensitivities of the price of derivative securities with respect to changes in the underlying asset price or parameters. The Malliavin calculus, the stochastic methods of variations, is one of the most popular tools used to derive Greeks. However, it is also very difficult to understand for most students and practitioners because it is based on complex mathematics. To help readers more easily understand the Malliavin calculus, the book introduces the discrete Malliavin calculus, a theory of the functional for the Bernoulli random walk. The discrete Malliavin calculus is significantly easier to understand, because the functional space of the Bernoulli random walk is realized in a finite dimensional space. As such, it makes this valuable tool far more accessible for a broad readership.

In diesem Buch lernen Sie die Vielfalt der Wechselwirkungen von Zufallsvariablen und unterschiedliche Ansätze ihrer theoretischen Beschreibung kennen. Im Fokus der Betrachtungen steht der Einsatz von Copulas; Sie lernen gezielt mit diesen zu operieren. Sie erfahren zunächst anhand umfangreicher Resultate und zahlreicher Beispiele, welche Ausprägungen von Abhängigkeitsstrukturen mit welchen Eigenschaften von Copulas korrespondieren. Anschließend lernen Sie ausgewählte Anwendungen der zuvor bereitgestellten Erkenntnisse kennen, die meist aus der Versicherungstechnik stammen. Sie erhalten beispielsweise einen Einblick in die speziellen Abhängigkeitsstrukturen in den Modellen der Erfahrungstarifierung. Eine Besonderheit stellt der Umgang mit Summen abhängiger Zufallsvariablen dar: Sie erfahren, wie auch ohne Vorliegen der häufigen Annahme der Unabhängigkeit die Verteilung der Summen bestimmt werden kann. Die Anwendungen und Untersuchungen werden zumeist durch Simulationen und Punktwolken illustriert.

Wavelets from a Statistical Perspective offers a modern, 2nd generation look on wavelets, far beyond the rigid setting of the equispaced, dyadic wavelets in the early days. With the methods of this book, based on the lifting scheme, researchers can set up a wavelet or another multiresolution analysis adapted to their data, ranging from images to scattered data or other irregularly spaced observations. Whereas classical wavelets stand a bit apart from other nonparametric methods, this book adds a multiscale touch to your spline, kernel or local polynomial smoothing procedure, thereby extending its applicability to nonlinear, nonparametric processing for piecewise smooth data.One of the chapters of the book constructs B-spline wavelets on nonequispaced knots and multiscale local polynomial transforms. In another chapter, the link between wavelets and Fourier analysis, ubiquitous in the classical approach, is explained, but without being inevitable. In further chapters the discrete wavelet transform is contrasted with the continuous version, the nondecimated (or maximal overlap) transform taking an intermediate position. An important principle in designing a wavelet analysis through the lifting scheme is finding the right balance between bias and variance. Bias and variance also play a crucial role in the nonparametric smoothing in a wavelet framework, in finding well working thresholds or other smoothing parameters. The numerous illustrations can be reproduced with the online available, accompanying software. The software and the exercises can also be used as a starting point in the further exploration of the material.