While good data is an enterprise asset, bad data is an enterprise liability. Data governance enables you to effectively and proactively manage data assets throughout the enterprise by providing guidance in the form of policies, standards, processes and rules and defining roles and responsibilities outlining who will do what, with respect to data. While implementing data governance is not rocket science, it is not a simple exercise. There is a lot confusion around what data governance is, and a lot of challenges in the implementation of data governance. Data governance is not a project or a one-off exercise but a journey that involves a significant amount of effort, time and investment and cultural change and a number of factors to take into consideration to achieve and sustain data governance success. Data Governance Success: Growing and Sustaining Data Governance is the third and final book in the Data Governance series and discusses the following: * Data governance perceptions and challenges * Key considerations when implementing data governance to achieve and sustain success* Strategy and data governance* Different data governance maturity frameworks* Data governance - people and process elements* Data governance metrics This book shares the combined knowledge related to data and data governance that the author has gained over the years of working in different industrial and research programs and projects associated with data, processes, and technologies and unique perspectives of Thought Leaders and Data Experts through Interviews conducted. This book will be highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge to support and succeed in data governance implementations. This book is technology agnostic and contains a balance of concepts and examples and illustrations making it easy for the readers to understand and relate to their own specific data projects.

This book is about random objects-sequences, processes, arrays, measures, functionals-with interesting symmetry properties. Here symmetry should beunderstoodinthebroadsenseofinvarianceunderafamily(notnecessarily a group) of measurable transformations. To be precise, it is not the random objects themselves but rather their distributions that are assumed to be symmetric. Though many probabilistic symmetries are conceivable and have been considered in various contexts, four of them-stationarity, contractability, exchangeability, and rotatability-stand out as especially interesting and - portant in several ways: Their study leads to some deep structural theorems of great beauty and signi?cance, they are intimately related to some basic areasofmodernprobabilitytheory, andtheyaremutuallyconnectedthrough a variety of basic relationships. The mentioned symmetries may be de?ned as invariance in distribution under shifts, contractions, permutations, and rotations. Stationarity being a familiar classical topic, treated extensively in many standard textbooks and monographs, most of our attention will be focused on the remaining three basic symmetries. The study of general probabilistic symmetries essentially originated with the work of de Finetti (1929-30), who proved by elementary means (no - vanced tools being yet available) the celebrated theorem named after him- the fact that every in?nite sequence of exchangeable events is mixed i.i.d.

Risk is the main source of uncertainty for investors, debtholders, corporate managers and other stakeholders. For all these actors, it is vital to focus on identifying and managing risk before making decisions. The success of their businesses depends on the relevance of their decisions and consequently, on their ability to manage and deal with the different types of risk. Accordingly, the main objective of this book is to promote scientific research in the different areas of risk management, aiming at being transversal and dealing with different aspects of risk management related to corporate finance as well as market finance. Thus, this book should provide useful insights for academics as well as professionals to better understand and assess the different types of risk.

Probabilistic approaches have played a prominent role in the study of complex physical systems for more than thirty years. This volume collects twenty articles on various topics in this field, including self-interacting random walks and polymer models in random and non-random environments, branching processes, Parisi formulas and metastability in spin glasses, and hydrodynamic limits for gradient Gibbs models. The majority of these articles contain original results at the forefront of contemporary research; some of them include review aspects and summarize the state-of-the-art on topical issues - one focal point is the parabolic Anderson model, which is considered with various novel aspects including moving catalysts, acceleration and deceleration and fron propagation, for both time-dependent and time-independent potentials. The authors are among the world's leading experts. This Festschrift honours two eminent researchers, Erwin Bolthausen and Jurgen Gartner, whose scientific work has profoundly influenced the field and all of the present contributions.

Methods of reasoning lying at the heart of rational scientific inference are explored and applied in some 55 papers by contributors from industry, defense establishments, and academia, brought together under the sponsorship of the US Navy and several European and American chemical corporations. The

The explosive development of information science and technology puts in new problems involving statistical data analysis. These problems result from higher re quirements concerning the reliability of statistical decisions, the accuracy of math ematical models and the quality of control in complex systems. A new aspect of statistical analysis has emerged, closely connected with one of the basic questions of cynergetics: how to "compress" large volumes of experimental data in order to extract the most valuable information from data observed. De tection of large "homogeneous" segments of data enables one to identify "hidden" regularities in an object's behavior, to create mathematical models for each seg ment of homogeneity, to choose an appropriate control, etc. Statistical methods dealing with the detection of changes in the characteristics of random processes can be of great use in all these problems. These methods have accompanied the rapid growth in data beginning from the middle of our century. According to a tradition of more than thirty years, we call this sphere of statistical analysis the "theory of change-point detection. " During the last fifteen years, we have witnessed many exciting developments in the theory of change-point detection. New promising directions of research have emerged, and traditional trends have flourished anew. Despite this, most of the results are widely scattered in the literature and few monographs exist. A real need has arisen for up-to-date books which present an account of important current research trends, one of which is the theory of non parametric change--point detection."

The volume represents presentations given at the 86th annual meeting of the Psychometric Society, held virtually on July 19-23, 2021. About 500 individuals contributed paper presentations, symposiums, poster presentations, pre-conference workshops, keynote presentations, and invited presentations. Since the 77th meeting, Springer has published the conference proceedings volume from this annual meeting to allow presenters to share their work and ideas with the wider research community, while still undergoing a thorough review process. This proceedings covers a diverse set of psychometric topics, including item response theory, Bayesian models, reliability, longitudinal measures, and cognitive diagnostic models.

The study of the statistics of extreme events is an essential first step in the mitigation of natural catastrophies, that often cause severe economic losses worldwide. This book is about the theoretical and practical aspects of the statistics of Extreme Events in Nature.Most importantly, this is the first text in which Copulas are introduced and used in Geophysics.Several topics are fully original, and show how standard models and calculations can be improved by exploiting the opportunities offered by Copulas. In addition, new quantities useful for design and risk assessment are introduced. Practicioners in all research areas of Geosciences and extreme events (including Finance and Insurance, closely related to natural disasters) will definitely benefit from the new Copula-approach outlined in the book.

Ordinal Data Modeling is a comprehensive treatment of ordinal data models from both likelihood and Bayesian perspectives. Written for graduate students and researchers in the statistical and social sciences, this book describes a coherent framework for understanding binary and ordinal regression models, item response models, graded response models, and ROC analyses, and for exposing the close connection between these models. A unique feature of this text is its emphasis on applications. All models developed in the book are motivated by real datasets, and considerable attention is devoted to the description of diagnostic plots and residual analyses. Software and datasets used for all analyses described in the text are available on websites listed in the preface.

Our time is characterized by an explosive growth in the use of ever more complicated and sophisticated (computer) models. These models rely on dynamical systems theory for the interpretation of their results and on probability theory for the quantification of their uncertainties. A conscientious and intelligent use of these models requires that both these theories are properly understood. This book is to provide such understanding. It gives a unifying treatment of dynamical systems theory and probability theory. It covers the basic concepts and statements of these theories, their interrelations, and their applications to scientific reasoning and physics. The book stresses the underlying concepts and mathematical structures but is written in a simple and illuminating manner without sacrificing too much mathematical rigor. The book is aimed at students, post-docs, and researchers in the applied sciences who aspire to better understand the conceptual and mathematical underpinnings of the models that they use. Despite the peculiarities of any applied science, dynamics and probability are the common and indispensable tools in any modeling effort. The book is self-contained, with many technical aspects covered in appendices, but does require some basic knowledge in analysis, linear algebra, and physics. Peter Muller, now a professor emeritus at the University of Hawaii, has worked extensively on ocean and climate models and the foundations of complex system theories.

This revised textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). The author presents information processing and communication systems that use algorithms based on probabilistic models and techniques, including web searches, digital links, speech recognition, GPS, route planning, recommendation systems, classification, and estimation. He then explains how these applications work and, along the way, provides the readers with the understanding of the key concepts and methods of applied probability. Python labs enable the readers to experiment and consolidate their understanding. The book includes homework, solutions, and Jupyter notebooks. This edition includes new topics such as Boosting, Multi-armed bandits, statistical tests, social networks, queuing networks, and neural networks. For ancillaries related to this book, including examples of Python demos and also Python labs used in Berkeley, please email Mary James at [email protected]. This is an open access book.

This book provides a comprehensive methodology to measure systemic risk in many of its facets and dimensions based on state-of-the-art risk assessment methods. Systemic risk has gained attention in the public eye since the collapse of Lehman Brothers in 2008. The bankruptcy of the fourth-biggest bank in the USA raised questions whether banks that are allowed to become "too big to fail" and "too systemic to fail" should carry higher capital surcharges on their size and systemic importance. The Global Financial Crisis of 2008-2009 was followed by the Sovereign Debt Crisis in the euro area that saw the first Eurozone government de facto defaulting on its debt and prompted actions at international level to stem further domino and cascade effects to other Eurozone governments and banks. Against this backdrop, a careful measurement of systemic risk is of utmost importance for the new capital regulation to be successful and for sovereign risk to remain in check. Most importantly, the book introduces a number of systemic fragility indicators for banks and sovereigns that can help to assess systemic risk and the impact of macroprudential and microprudential policies.

Fully updated with the latest developments in the eigenvalue Monte Carlo calculations and automatic variance reduction techniques and containing an entirely new chapter on fission matrix and alternative hybrid techniques. This second edition explores the uses of the Monte Carlo method for real-world applications, explaining its concepts and limitations. Featuring illustrative examples, mathematical derivations, computer algorithms, and homework problems, it is an ideal textbook and practical guide for nuclear engineers and scientists looking into the applications of the Monte Carlo method, in addition to students in physics and engineering, and those engaged in the advancement of the Monte Carlo methods. Describes general and particle-transport-specific automated variance reduction techniques Presents Monte Carlo particle transport eigenvalue issues and methodologies to address these issues Presents detailed derivation of existing and advanced formulations and algorithms with real-world examples from the author's research activities

This handbook will provide both overviews of statistical methods in sports and in-depth treatment of critical problems and challenges confronting statistical research in sports. The material in the handbook will be organized by major sport (baseball, football, hockey, basketball, and soccer) followed by a section on other sports and general statistical design and analysis issues that are common to all sports. This handbook has the potential to become the standard reference for obtaining the necessary background to conduct serious statistical analyses for sports applications and to appreciate scholarly work in this expanding area.

This edited collection brings together internationally recognized experts in a range of areas of statistical science to honor the contributions of the distinguished statistician, Barry C. Arnold. A pioneering scholar and professor of statistics at the University of California, Riverside, Dr. Arnold has made exceptional advancements in different areas of probability, statistics, and biostatistics, especially in the areas of distribution theory, order statistics, and statistical inference. As a tribute to his work, this book presents novel developments in the field, as well as practical applications and potential future directions in research and industry. It will be of interest to graduate students and researchers in probability, statistics, and biostatistics, as well as practitioners and technicians in the social sciences, economics, engineering, and medical sciences.

Networks of queues arise frequently as models for a wide variety of congestion phenomena. Discrete event simulation is often the only available means for studying the behavior of complex networks and many such simulations are non Markovian in the sense that the underlying stochastic process cannot be repre sented as a continuous time Markov chain with countable state space. Based on representation of the underlying stochastic process of the simulation as a gen eralized semi-Markov process, this book develops probabilistic and statistical methods for discrete event simulation of networks of queues. The emphasis is on the use of underlying regenerative stochastic process structure for the design of simulation experiments and the analysis of simulation output. The most obvious methodological advantage of simulation is that in principle it is applicable to stochastic systems of arbitrary complexity. In practice, however, it is often a decidedly nontrivial matter to obtain from a simulation information that is both useful and accurate, and to obtain it in an efficient manner. These difficulties arise primarily from the inherent variability in a stochastic system, and it is necessary to seek theoretically sound and computationally efficient methods for carrying out the simulation. Apart from implementation consider ations, important concerns for simulation relate to efficient methods for generating sample paths of the underlying stochastic process. the design of simulation ex periments, and the analysis of simulation output."

Presents an important and unique introduction to random walk theory Random walk is a stochastic process that has proven to be a useful model in understanding discrete-state discrete-time processes across a wide spectrum of scientific disciplines. Elements of Random Walk and Diffusion Processes provides an interdisciplinary approach by including numerous practical examples and exercises with real-world applications in operations research, economics, engineering, and physics. Featuring an introduction to powerful and general techniques that are used in the application of physical and dynamic processes, the book presents the connections between diffusion equations and random motion. Standard methods and applications of Brownian motion are addressed in addition to Levy motion, which has become popular in random searches in a variety of fields. The book also covers fractional calculus and introduces percolation theory and its relationship to diffusion processes. With a strong emphasis on the relationship between random walk theory and diffusion processes, Elements of Random Walk and Diffusion Processes features: * Basic concepts in probability, an overview of stochastic and fractional processes, and elements of graph theory * Numerous practical applications of random walk across various disciplines, including how to model stock prices and gambling, describe the statistical properties of genetic drift, and simplify the random movement of molecules in liquids and gases * Examples of the real-world applicability of random walk such as node movement and node failure in wireless networking, the size of the Web in computer science, and polymers in physics * Plentiful examples and exercises throughout that illustrate the solution of many practical problems Elements of Random Walk and Diffusion Processes is an ideal reference for researchers and professionals involved in operations research, economics, engineering, mathematics, and physics. The book is also an excellent textbook for upper-undergraduate and graduate level courses in probability and stochastic processes, stochastic models, random motion and Brownian theory, random walk theory, and diffusion process techniques.

This book presents innovations in the mathematical foundations of financial analysis and numerical methods for finance and applications to the modeling of risk. The topics selected include measures of risk, credit contagion, insider trading, information in finance, stochastic control and its applications to portfolio choices and liquidation, models of liquidity, pricing, and hedging. The models presented are based on the use of Brownian motion, Levy processes and jump diffusions. Moreover, fractional Brownian motion and ambit processes are also introduced at various levels. The chosen blend of topics gives an overview of the frontiers of mathematics for finance. New results, new methods and new models are all introduced in different forms according to the subject. Additionally, the existing literature on the topic is reviewed. The diversity of the topics makes the book suitable for graduate students, researchers and practitioners in the areas of financial modeling and quantitative finance. The chapters will also be of interest to experts in the financial market interested in new methods and products. This volume presents the results of the European ESF research networking program Advanced Mathematical Methods for Finance.

Classical Methods of Statistics is a guidebook combining theory and practical methods. It is especially conceived for graduate students and scientists who are interested in the applications of statistical methods to plasma physics. Thus it provides also concise information on experimental aspects of fusion-oriented plasma physics. In view of the first three basic chapters it can be fruitfully used by students majoring in probability theory and statistics. The first part deals with the mathematical foundation and framework of the subject. Some attention is given to the historical background. Exercises are added to help readers understand the underlying concepts. In the second part, two major case studies are presented which exemplify the areas of discriminant analysis and multivariate profile analysis, respectively. To introduce these case studies, an outline is provided of the context of magnetic plasma fusion research. In the third part an overview is given of statistical software; separate attention is devoted to SAS and S-PLUS. The final chapter presents several datasets and gives a description of their physical setting. Most of these datasets were assembled at the ASDEX Upgrade Tokamak. All of them are accompanied by exercises in form of guided (minor) case studies. The book concludes with translations of key concepts into several languages.

This book includes a wide selection of the papers presented at the 48th Scientific Meeting of the Italian Statistical Society (SIS2016), held in Salerno on 8-10 June 2016. Covering a wide variety of topics ranging from modern data sources and survey design issues to measuring sustainable development, it provides a comprehensive overview of the current Italian scientific research in the fields of open data and big data in public administration and official statistics, survey sampling, ordinal and symbolic data, statistical models and methods for network data, time series forecasting, spatial analysis, environmental statistics, economic and financial data analysis, statistics in the education system, and sustainable development. Intended for researchers interested in theoretical and empirical issues, this volume provides interesting starting points for further research.

This book is the second edition of Facet Theory and the Mapping Sentence: Evolving Philosophy, Use and Application (2014). It consolidates the qualitative and quantitative research positions of facet theory and delves deeper into their qualitative application in psychology, social and the behavioural sciences and in the humanities. In their traditional quantitative guise, facet theory and its mapping sentence incorporate multi-dimensional statistics. They are also a way of thinking systematically and thoroughly about the world. The book is particularly concerned with the development of the declarative mapping sentence as a tool and an approach to qualitative research. The evolution of the facet theory approach is presented along with many examples of its use in a wide variety of research domains. Since the first edition, the major advance in facet theory has been the formalization of the use of the declarative mapping sentence and this is given a prominent position in the new edition. The book will be compelling reading for students at all levels and for academics and research professionals from the humanities, social sciences and behavioural sciences.

Mathematical and Statistical Estimation Approaches in Epidemiology compiles t- oretical and practical contributions of experts in the analysis of infectious disease epidemics in a single volume. Recent collections have focused in the analyses and simulation of deterministic and stochastic models whose aim is to identify and rank epidemiological and social mechanisms responsible for disease transmission. The contributions in this volume focus on the connections between models and disease data with emphasis on the application of mathematical and statistical approaches that quantify model and data uncertainty. The book is aimed at public health experts, applied mathematicians and sci- tists in the life and social sciences, particularly graduate or advanced undergraduate students, who are interested not only in building and connecting models to data but also in applying and developing methods that quantify uncertainty in the context of infectious diseases. Chowell and Brauer open this volume with an overview of the classical disease transmission models of Kermack-McKendrick including extensions that account for increased levels of epidemiological heterogeneity. Their theoretical tour is followed by the introduction of a simple methodology for the estimation of, the basic reproduction number,R . The use of this methodology 0 is illustrated, using regional data for 1918-1919 and 1968 in uenza pandemics.

This book presents a framework for developing as well as a comprehensive collection of state-of-the-art process querying methods. Process querying combines concepts from Big Data and Process Modeling and Analysis with Business Process Intelligence and Process Analytics to study techniques for retrieving and manipulating models of real-world and envisioned processes to organize and extract process-related information for subsequent systematic use. The book comprises sixteen contributed chapters distributed over four parts and two auxiliary chapters. The auxiliary chapters by the editor provide an introduction to the area of process querying and a summary of the presented methods, techniques, and applications for process querying. The introductory chapter also examines a process querying framework. The contributed chapters present various process querying methods, including discussions on how they instantiate the framework components, thus supporting the comparison of the methods. The four parts are due to the distinctive features of the methods they include. The first three are devoted to querying event logs generated by IT-systems that support business processes at organizations, querying process designs captured in process models, and methods that address querying both event logs and process models. The methods in these three parts usually define a language for specifying process queries. The fourth part discusses methods that operate over inputs other than event logs and process models, e.g., streams of process events, or do not develop dedicated languages for specifying queries, e.g., methods for assessing process model similarity. This book is mainly intended for researchers. All the chapters in this book are contributed by active researchers in the research disciplines of business process management, process mining, and process querying. They describe state-of-the-art methods for process querying, discuss use cases of process querying, and suggest directions for future work for advancing the field. Yet, also other groups like business or data scientists and other professionals, lecturers, graduate students, and tool vendors will find relevant information for their distinctive needs. Chapter "Celonis PQL: A Query Language for Process Mining" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

The interaction of various ideas from different researchers provides a main impetus to mathematical prosress. An important way to make communication possible is through international conferences on more or less spezialized topics~ The existence of several centers for research in probabil ity and statistics in the eastern part of central Europe - somewhat vaguely described as the Pannonian area - led to the idea of organizing Pannonian Symposia on Mathematical Statistics (PS~1S). The second such symposium was held at Bad Tatzmannsdorf, Burgenland (Austria), from 14 to 20 June 1981. About 100 researchers from 13 countries participated in that event and about 70 papers were delivered. Most of the papers dealt with one of the following topics: nonparametric estimation theory, asymptotic theory of estimation, invariance principles, limit theorems and aoplications. Full versions of selected papers, all presenting new results are included in this volume. The editors take this opportunity to thank the following institutions for their assistance in making the conference possible: the Provincial Government of Burgenland, the Austrian Ministry for Research and Science, the Burgenland Chamber of Commerce, the Control Data Corporation, the Austrian Society for Statistics and Informatics, the Landes- hypothekenbank Burgenland, the Volksbank Oberwart, and the Community and Kurbad AG of Bad Tatzmannsdorf. We are also greatly indebted to all those persons who helped in editing this volume and in particular to the vii W. Grossmann et al. reds.), Probability and Statistical Inference, vii-viii.

This book describes concepts and tools needed for water resources management, including methods for modeling, simulation, optimization, big data analysis, data mining, remote sensing, geographical information system, game theory, conflict resolution, System dynamics, agent-based models, multiobjective, multicriteria, and multiattribute decision making and risk and uncertainty analysis, for better and sustainable management of water resources and consumption, thus mitigating the present and future global water shortage crisis. It presents the applications of these tools through case studies which demonstrate its benefits of proper management of water resources systems. This book acts as a reference for students, professors, industrial practitioners, and stakeholders in the field of water resources and hydrology.