Sektion 1: Internet als Business-Plattform.- Elektronische Koordination interorganisatorischer Geschaftsprozesse zwischen privaten Haushalten und Versicherungen im Rahmen von Tele-Insuring.- Internet-Nutzung im Business-to-Business-Bereich: Stand der Entwicklung, Typologie und Anwendungsbeispiele.- Aufbau eines Elektronischen Handelsplatzes fur Java-Applets.- Preisstrategien fur ein integriertes Universal-Internet.- Sektion 2: Optimierung von Geschaftsprozessen.- Value-Based Management of Inter-Organizational Business Processes.- A Hierarchical Planning Procedure Supporting the Selection of Service Providers in Outtasking Decisions.- A Multiagent System-Approach for the Design of Information Systems in Virtual Organizations.- Office-Automation in Municipal and County Administration with an Integrated Workflow Based Information System.- Efficiency and Cost Implications of Capital Allocation Mechanisms: A Contribution to the Market-versus-Hierarchy-Discussion.- Sektion 3: Groupware- und Workflow-Strategien.- Organisatorischer Wandel bei Einfuhrung von Groupware.- Increased Competitiveness using a Groupware based Project Controlling System.- Architektur zur informationstechnologischen Unterstutzung von Kooperationen.- Enterprise Knowledge Medium (EKM) - Konzeption und Einsatz eines computergestutzten Planungs- und Kontrollsystems im prozessorientierten Unternehmen.- Unterstutzung der Workflow-Entwicklung durch ein unternehmensweites Repository fur Geschaftsprozessrealisierungen.- Sektion 4: Groupware-Anwendungen.- New perspectives for higher education processes as a team-based approach - Back-office information technology for higher education and training.- Konfiguration des Informationsdienstes in Groupware.- Sektion 5: Wirtschaftliche Programmerstellung.- Global Production: The Case of Offshore Programming.- Metriken fur die IV-Diagnose, Konzept und prototypische Implementierung.- Produktinformationssysteme in Business Networks.- Sektion 6: SAP und Client-Server-Integration.- System Migration and System Integration: Two SAP Cases.- Unternehmensweite Datenkonsistenz durch Integration bestehender Informationssysteme.- Client/Server Architecture: what it promises - what it really provides.- IS Project Risk in Polish Organizations.- Sektion 7: Organisation und Datenmanagement.- Flexible Organizations Through Object-oriented and Transaction-oriented Information Systems.- Determinants and Outcomes of Electronic Data Interchange Integration.- Referenz-Informationsmodelle fur den Handel: Begriff, Nutzen und Empfehlungen fur die Gestaltung und unternehmensspezifische Adaption von Referenzmodellen.- Entwicklung eines Data Warehouse fur das Produktionscontrolling: Konzepte und Erfahrungen.- Sektion 8: Neue Chancen durch Multimedia.- Der Markt fur interaktive elektronische Medien aus oekonomischer Sicht.- Sektion 9: Anwendungen von Internet/Intranet.- Improving Competitiveness of Direct Banking via IT-Enabled Incentive Schemes.- Die Nutzung von Internet-Diensten im Rahmen des Elektronischen Datenaustauschs - Architekturvarianten und ein Anwendungsszenario.- Sektion 10: Organisation und Workflow.- Neue Organisationsformen und IT: Herausforderung fur die Unternehmensgestalter.- INCOME/WF - A Petri Net Based Approach to Workflow Management.- On the Object-Oriented Modelling of Distributed Workflow Applications.- Sektion 11: Reorganisation des Unternehmens.- Synthesizing Business and Information Systems (IS): Towards a Common Business-IS Model based on Agents.- Planungs- und Kontrollmodelle zur Steuerung prozessorientierter Organisationen auf der Basis einer Intranet-Anwendung.- Neue Kernprozesse fur Versicherer mit Agenturnetz.- Sponsorenverzeichnis.- Autoren- und Adressverzeichnis.

This book provides practical applications of doubly classified models by using R syntax to generate the models. It also presents these models in symbolic tables so as to cater to those who are not mathematically inclined, while numerous examples throughout the book illustrate the concepts and their applications. For those who are not aware of this modeling approach, it serves as a good starting point to acquire a basic understanding of doubly classified models. It is also a valuable resource for academics, postgraduate students, undergraduates, data analysts and researchers who are interested in examining square contingency tables.

Global markets and competition have forced companies to operate in a physically distributed environment to take the advantage of benefits of strategic alliances between partnering firms. With global operations in place, there is a need for suitable Enterprise Information Systems (EIS) such as Enterprise Resource Planning (ERP) and E-Commerce (EC) for the integration of extended enterprises along the supply chain with the objective of achieving flexibility and responsiveness. Enterprise Information Systems and Advancing Business Solutions: Emerging Models is to provide comprehensive coverage and understanding of various enterprise information systems (EIS) such as enterprise resource planning (ERP) and electronic commerce (EC) and their implications on supply chain management and organizational competitiveness. Design, development, and implementation issues of EIS including ERP and EC will be discussed. These include organizational, people, and technological issues of EIS. This title will also expand the knowledge on ERP and EC and in turn help researchers and practitioners to develop suitable strategies, tactics, and operational policies for EIS and for improving communication in organizations.

This book offers postgraduate and early career researchers in accounting and information systems a guide to choosing, executing and reporting appropriate data analysis methods to answer their research questions. It provides readers with a basic understanding of the steps that each method involves, and of the facets of the analysis that require special attention. Rather than presenting an exhaustive overview of the methods or explaining them in detail, the book serves as a starting point for developing data analysis skills: it provides hands-on guidelines for conducting the most common analyses and reporting results, and includes pointers to more extensive resources. Comprehensive yet succinct, the book is brief and written in a language that everyone can understand - from students to those employed by organizations wanting to study the context in which they work. It also serves as a refresher for researchers who have learned data analysis techniques previously but who need a reminder for the specific study they are involved in.

The advent of high-speed, affordable computers in the last two decades has given a new boost to the nonparametric way of thinking. Classical nonparametric procedures, such as function smoothing, suddenly lost their abstract flavour as they became practically implementable. In addition, many previously unthinkable possibilities became mainstream; prime examples include the bootstrap and resampling methods, wavelets and nonlinear smoothers, graphical methods, data mining, bioinformatics, as well as the more recent algorithmic approaches such as bagging and boosting. This volume is a collection of short articles - most of which having a review component - describing the state-of-the art of Nonparametric Statistics at the beginning of a new millennium.

Key features:

algorithic approaches
wavelets and nonlinear smoothers
graphical methods and data mining
biostatistics and bioinformatics
bagging and boosting
support vector machines
resampling methods
"

This book presents a comprehensive study of multivariate time series with linear state space structure. The emphasis is put on both the clarity of the theoretical concepts and on efficient algorithms for implementing the theory. In particular, it investigates the relationship between VARMA and state space models, including canonical forms. It also highlights the relationship between Wiener-Kolmogorov and Kalman filtering both with an infinite and a finite sample. The strength of the book also lies in the numerous algorithms included for state space models that take advantage of the recursive nature of the models. Many of these algorithms can be made robust, fast, reliable and efficient. The book is accompanied by a MATLAB package called SSMMATLAB and a webpage presenting implemented algorithms with many examples and case studies. Though it lays a solid theoretical foundation, the book also focuses on practical application, and includes exercises in each chapter. It is intended for researchers and students working with linear state space models, and who are familiar with linear algebra and possess some knowledge of statistics.

This volume of selected and peer-reviewed contributions on the latest developments in time series analysis and forecasting updates the reader on topics such as analysis of irregularly sampled time series, multi-scale analysis of univariate and multivariate time series, linear and non-linear time series models, advanced time series forecasting methods, applications in time series analysis and forecasting, advanced methods and online learning in time series and high-dimensional and complex/big data time series. The contributions were originally presented at the International Work-Conference on Time Series, ITISE 2016, held in Granada, Spain, June 27-29, 2016. The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing the disciplines of computer science, mathematics, statistics and econometrics.

This book presents the latest research on the statistical analysis of functional, high-dimensional and other complex data, addressing methodological and computational aspects, as well as real-world applications. It covers topics like classification, confidence bands, density estimation, depth, diagnostic tests, dimension reduction, estimation on manifolds, high- and infinite-dimensional statistics, inference on functional data, networks, operatorial statistics, prediction, regression, robustness, sequential learning, small-ball probability, smoothing, spatial data, testing, and topological object data analysis, and includes applications in automobile engineering, criminology, drawing recognition, economics, environmetrics, medicine, mobile phone data, spectrometrics and urban environments. The book gathers selected, refereed contributions presented at the Fifth International Workshop on Functional and Operatorial Statistics (IWFOS) in Brno, Czech Republic. The workshop was originally to be held on June 24-26, 2020, but had to be postponed as a consequence of the COVID-19 pandemic. Initiated by the Working Group on Functional and Operatorial Statistics at the University of Toulouse in 2008, the IWFOS workshops provide a forum to discuss the latest trends and advances in functional statistics and related fields, and foster the exchange of ideas and international collaboration in the field.

Growth curve models in longitudinal studies are widely used to model population size, body height, biomass, fungal growth, and other variables in the biological sciences, but these statistical methods for modeling growth curves and analyzing longitudinal data also extend to general statistics, economics, public health, demographics, epidemiology, SQC, sociology, nano-biotechnology, fluid mechanics, and other applied areas. There is no one-size-fits-all approach to growth measurement. The selected papers in this volume build on presentations from the GCM workshop held at the Indian Statistical Institute, Giridih, on March 28-29, 2016. They represent recent trends in GCM research on different subject areas, both theoretical and applied. This book includes tools and possibilities for further work through new techniques and modification of existing ones. The volume includes original studies, theoretical findings and case studies from a wide range of applied work, and these contributions have been externally refereed to the high quality standards of leading journals in the field.

The purpose of this book is to thoroughly prepare the reader for applied research in clustering. Cluster analysis comprises a class of statistical techniques for classifying multivariate data into groups or clusters based on their similar features. Clustering is nowadays widely used in several domains of research, such as social sciences, psychology, and marketing, highlighting its multidisciplinary nature. This book provides an accessible and comprehensive introduction to clustering and offers practical guidelines for applying clustering tools by carefully chosen real-life datasets and extensive data analyses. The procedures addressed in this book include traditional hard clustering methods and up-to-date developments in soft clustering. Attention is paid to practical examples and applications through the open source statistical software R. Commented R code and output for conducting, step by step, complete cluster analyses are available. The book is intended for researchers interested in applying clustering methods. Basic notions on theoretical issues and on R are provided so that professionals as well as novices with little or no background in the subject will benefit from the book.

This uniquely accessible book helps readers use CABology to solve real-world business problems and drive real competitive advantage. It provides reliable, concise information on the real benefits, usage and operationalization aspects of utilizing the "Trio Wave" of cloud, analytic and big data. Anyone who thinks that the game changing technology is slow paced needs to think again. This book opens readers' eyes to the fact that the dynamics of global technology and business are changing. Moreover, it argues that businesses must transform themselves in alignment with the Trio Wave if they want to survive and excel in the future. CABology focuses on the art and science of optimizing the business goals to deliver true value and benefits to the customer through cloud, analytic and big data. It offers business of all sizes a structured and comprehensive way of discovering the real benefits, usage and operationalization aspects of utilizing the Trio Wave.

This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvagar, Lofoten, Norway, in May 2014. The focus of the symposium was on statistical and machine learning methodologies specifically developed for inference in "big data" situations, with particular reference to genomic applications. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection. Highlighting cutting-edge research and casting light on future research directions, the contributions will benefit graduate students and researchers in computational biology, statistics and the machine learning community.

This book is a selection of peer-reviewed contributions presented at the third Bayesian Young Statisticians Meeting, BAYSM 2016, Florence, Italy, June 19-21. The meeting provided a unique opportunity for young researchers, M.S. students, Ph.D. students, and postdocs dealing with Bayesian statistics to connect with the Bayesian community at large, to exchange ideas, and to network with others working in the same field. The contributions develop and apply Bayesian methods in a variety of fields, ranging from the traditional (e.g., biostatistics and reliability) to the most innovative ones (e.g., big data and networks).

Auf der Grundlage aktueller Entwicklungen der Wirtschaftsinformatik stellen die Autoren Konzepte dar, die als Richtlinie fA1/4r die kA1/4nftige Entwicklung von einem die operationalen Systeme, Data Warehouse-, Informations-, Experten- und Data Mining Systeme umfassenden Anwendungssystem dienen kAnnen.
Neben notwendigen theoretischen Aspekten wird basierend auf der umfangreichen praktischen Erfahrung der Autoren in der Anwendungsentwicklung fA1/4r Kreditinstitute ein konkreter Praxisbezug hergestellt. Die Autoren untermauern ihre Betrachtungen heutiger und kA1/4nftiger Anwendungsentwicklungen sowohl mit den theoretischen Grundlagen als auch mit dem praktischen Nutzen der Methoden. Mit konkretem Praxisbezug werden flexible operationale Systeme erlAutert und eine optimale Informationsversorgung der EntscheidungstrAger aufgezeigt. AussagefAhige Grafiken machen das Buch fA1/4r jeden Interessierten verstAndlich.

This book offers an original and broad exploration of the fundamental methods in Clustering and Combinatorial Data Analysis, presenting new formulations and ideas within this very active field. With extensive introductions, formal and mathematical developments and real case studies, this book provides readers with a deeper understanding of the mutual relationships between these methods, which are clearly expressed with respect to three facets: logical, combinatorial and statistical. Using relational mathematical representation, all types of data structures can be handled in precise and unified ways which the author highlights in three stages: Clustering a set of descriptive attributes Clustering a set of objects or a set of object categories Establishing correspondence between these two dual clusterings Tools for interpreting the reasons of a given cluster or clustering are also included. Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering will be a valuable resource for students and researchers who are interested in the areas of Data Analysis, Clustering, Data Mining and Knowledge Discovery.

This book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latest developments and trends in statistical inference for students who do not have an advanced mathematical and statistical background. The topics discussed in the book are fundamental and common to many fields of statistical inference and thus serve as a point of departure for in-depth study. The book is divided into eight chapters: Chapter 1 provides an overview of topics on sufficiency and completeness, while Chapter 2 briefly discusses unbiased estimation. Chapter 3 focuses on the study of moments and maximum likelihood estimators, and Chapter 4 presents bounds for the variance. In Chapter 5, topics on consistent estimator are discussed. Chapter 6 discusses Bayes, while Chapter 7 studies some more powerful tests. Lastly, Chapter 8 examines unbiased and other tests. Senior undergraduate and graduate students in statistics and mathematics, and those who have taken an introductory course in probability, will greatly benefit from this book. Students are expected to know matrix algebra, calculus, probability and distribution theory before beginning this course. Presenting a wealth of relevant solved and unsolved problems, the book offers an excellent tool for teachers and instructors who can assign homework problems from the exercises, and students will find the solved examples hugely beneficial in solving the exercise problems.

This book covers original research and the latest advances in symbolic, algebraic and geometric computation; computational methods for differential and difference equations, symbolic-numerical computation; mathematics software design and implementation; and scientific and engineering applications based on features, invited talks, special sessions and contributed papers presented at the 9th (in Fukuoka, Japan in 2009) and 10th (in Beijing China in 2012) Asian Symposium on Computer Mathematics (ASCM). Thirty selected and refereed articles in the book present the conference participants' ideas and views on researching mathematics using computers.

This book is a comprehensive guide to qualitative comparative analysis (QCA) using R. Using Boolean algebra to implement principles of comparison used by scholars engaged in the qualitative study of macro social phenomena, QCA acts as a bridge between the quantitative and the qualitative traditions. The QCA package for R, created by the author, facilitates QCA within a graphical user interface. This book provides the most current information on the latest version of the QCA package, which combines written commands with a cross-platform interface. Beginning with a brief introduction to the concept of QCA, this book moves from theory to calibration, from analysis to factorization, and hits on all the key areas of QCA in between. Chapters one through three are introductory, familiarizing the reader with R, the QCA package, and elementary set theory. The next few chapters introduce important applications of the package beginning with calibration, analysis of necessity, analysis of sufficiency, parameters of fit, negation and factorization, and the construction of Venn diagrams. The book concludes with extensions to the classical package, including temporal applications and panel data. Providing a practical introduction to an increasingly important research tool for the social sciences, this book will be indispensable for students, scholars, and practitioners interested in conducting qualitative research in political science, sociology, business and management, and evaluation studies.