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Applications in Statistical Computing
This volume presents a selection of research papers on various topics at the interface of statistics and computer science. Emphasis is put on the practical applications of statistical methods in various disciplines, using machine learning and other computational methods. The book covers fields of research including the design of experiments, computational statistics, music data analysis, statistical process control, biometrics, industrial engineering, and econometrics. Gathering innovative, high-quality and scientifically relevant contributions, the volume was published in honor of Claus Weihs, Professor of Computational Statistics at TU Dortmund University, on the occasion of his 66th birthday.
Statistics Today
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Data Analysis, Machine Learning and Knowledge Discovery
Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medicine, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and knowledge discovery presented during the 36th annual conference of the German Classification Society (GfKl). The conference was held at the University of Hildesheim (Germany) in August 2012.
German Medical Data Sciences 2022 - Future Medicine: More Precise, More Integrative, More Sustainable!
The aim of medical research has always been to gain scientific knowledge which will serve to improve the diagnosis, therapy and prevention of diseases. It is also becoming increasingly important to take account of the changing circumstances of medical care. Factors such as the ageing of society and the recent pandemic have not only led to greater use of medical care, but have also put the human resources and infrastructural basis of the health system under great pressure. Such developments call for science-based solutions which can better adapt medical action to the needs of patients to ensure that medicine remains affordable and accessible for all. This book is the 6th volume of the German ...
Analysis of Large and Complex Data
This book offers a snapshot of the state-of-the-art in classification at the interface between statistics, computer science and application fields. The contributions span a broad spectrum, from theoretical developments to practical applications; they all share a strong computational component. The topics addressed are from the following fields: Statistics and Data Analysis; Machine Learning and Knowledge Discovery; Data Analysis in Marketing; Data Analysis in Finance and Economics; Data Analysis in Medicine and the Life Sciences; Data Analysis in the Social, Behavioural, and Health Care Sciences; Data Analysis in Interdisciplinary Domains; Classification and Subject Indexing in Library and Information Science. The book presents selected papers from the Second European Conference on Data Analysis, held at Jacobs University Bremen in July 2014. This conference unites diverse researchers in the pursuit of a common topic, creating truly unique synergies in the process.
Algorithms from and for Nature and Life
This volume provides approaches and solutions to challenges occurring at the interface of research fields such as, e.g., data analysis, data mining and knowledge discovery, computer science, operations research, and statistics. In addition to theory-oriented contributions various application areas are included. Moreover, traditional classification research directions concerning network data, graphs, and social relationships as well as statistical musicology describe examples for current interest fields tackled by the authors. The book comprises a total of 55 selected papers presented at the Joint Conference of the German Classification Society (GfKl), the German Association for Pattern Recognition (DAGM), and the Symposium of the International Federation of Classification Societies (IFCS) in 2011.
The Oxford Handbook of Corporate Social Responsibility
CSR encompasses broad questions about the changing relationship between business, society, and government. An authoritative review of the academic research that has both prompted, and responded to, these issues, the text provides clear thinking and perspectives on CSR and the debates around it.
Learning and Intelligent Optimization
This book constitutes the thoroughly refereed post-conference proceedings of the 8th International Conference on Learning and Optimization, LION 8, which was held in Gainesville, FL, USA, in February 2014. The 33 contributions presented were carefully reviewed and selected for inclusion in this book. A large variety of topics are covered, such as algorithm configuration; multiobjective optimization; metaheuristics; graphs and networks; logistics and transportation; and biomedical applications.
International Conference on Advanced Intelligent Systems for Sustainable Development
This book describes the potential contributions of emerging technologies in different fields as well as the opportunities and challenges related to the integration of these technologies in the socio-economic sector. In this book, many latest technologies are addressed, particularly in the fields of computer science and engineering. The expected scientific papers covered state-of-the-art technologies, theoretical concepts, standards, product implementation, ongoing research projects, and innovative applications of Sustainable Development. This new technology highlights, the guiding principle of innovation for harnessing frontier technologies and taking full profit from the current technological revolution to reduce gaps that hold back truly inclusive and sustainable development. The fundamental and specific topics are Big Data Analytics, Wireless sensors, IoT, Geospatial technology, Engineering and Mechanization, Modeling Tools, Risk analytics, and preventive systems.
The Performance of Socially Responsible Investment Funds in Europe
Socially responsible investing (SRI) is an investment approach that combines investors’ financial as well as nonfinancial goals in the security selection process. Technically, investors can engage in SRI either by directly investing in companies that implement corporate social activities or by investing their money in SRI funds, which apply screening criteria to select securities. The screening process applied by the SRI funds has led to controversy among academics regarding whether the use of SRI screens in the security selection process influences the financial performance of the funds. The empirical study analyzes whether or not the screening process applied by such funds influences the...
