Contributed by world renowned researchers, the book features a wide range of important topics in modern statistical theory and methodology, economics and finance, ecology, education, health and sports studies, and computer and IT-data mining. It is accessible to students and of interest to experts.Many of the contributions are concerned with theoretical innovations, but all have applications in view, and some contain illustrations of the applied methods or photos of historic mathematicians.A few of the notable contributors are Ejaz Ahmed (Windsor), Joe Gani (ANU), Roger Gay (Monash), Atsuhiro Hayashi (NCUEE, Tokyo), Markus Hegland (ANU), Chris Heyde (ANU/Columbia), Jeff Hunter (Massey), Phil Lewis (Canberra), Heinz Neudecker (Amsterdam), Graham Pollard (Canberra), Simo Puntanen (Tampere), George Styan (McGill), and Goetz Trenkler (Dortmund).

This volume features selected, refereed papers on various aspects of statistics, matrix theory and its applications to statistics, as well as related numerical linear algebra topics and numerical solution methods, which are relevant for problems arising in statistics and in big data. The contributions were originally presented at the 25th International Workshop on Matrices and Statistics (IWMS 2016), held in Funchal (Madeira), Portugal on June 6-9, 2016. The IWMS workshop series brings together statisticians, computer scientists, data scientists and mathematicians, helping them better understand each other’s tools, and fostering new collaborations at the interface of matrix theory and statistics.

The four-volume set LNAI 6881-LNAI 6884 constitutes the refereed proceedings of the 15th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2011, held in Kaiserslautern, Germany, in September 2011. Part 2: The total of 244 high-quality papers presented were carefully reviewed and selected from numerous submissions. The 70 papers of Part 2 are organized in topical sections on web intelligence, text and multimedia mining and retrieval, intelligent tutoring systems and e-learning environments, other / misc. intelligent systems topics, methods and techniques of artificial and computational intelligence in economics, finance and decision making, workshop on seamless integration of semantic technologies in computer-supported office work (sistcow), innovations in chance discovery, advanced knowledge-based systems, recent trends in knowledge engineering, smart systems, and their applications.

In 1945, very early in the history of the development of a rigorous analytical theory of probability, Feller (1945) wrote a paper called “The fundamental limit theorems in probability” in which he set out what he considered to be “the two most important limit theorems in the modern theory of probability: the central limit theorem and the recently discovered ... ‘Kolmogoroff’s cel ebrated law of the iterated logarithm’ ”. A little later in the article he added to these, via a charming description, the “little brother (of the central limit theo rem), the weak law of large numbers”, and also the strong law of large num bers, which he considers as a close relative of the law of the iterated logarithm. Feller might well have added to these also the beautiful and highly applicable results of renewal theory, which at the time he himself together with eminent colleagues were vigorously producing. Feller’s introductory remarks include the visionary: “The history of probability shows that our problems must be treated in their greatest generality: only in this way can we hope to discover the most natural tools and to open channels for new progress. This remark leads naturally to that characteristic of our theory which makes it attractive beyond its importance for various applications: a combination of an amazing generality with algebraic precision.

This Festschrift is dedicated to Götz Trenkler on the occasion of his 65th birthday. As can be seen from the long list of contributions, Götz has had and still has an enormous range of interests, and colleagues to share these interests with. He is a leading expert in linear models with a particular focus on matrix algebra in its relation to statistics. He has published in almost all major statistics and matrix theory journals. His research activities also include other areas (like nonparametrics, statistics and sports, combination of forecasts and magic squares, just to mention afew). Götz Trenkler was born in Dresden in 1943. After his school years in East G- many and West-Berlin, he obtained a Diploma in Mathematics from Free University of Berlin (1970), where he also discovered his interest in Mathematical Statistics. In 1973, he completed his Ph.D. with a thesis titled: On a distance-generating fu- tion of probability measures. He then moved on to the University of Hannover to become Lecturer and to write a habilitation-thesis (submitted 1979) on alternatives to the Ordinary Least Squares estimator in the Linear Regression Model, a topic that would become his predominant ?eld of research in the years to come.

In teaching linear statistical models to first-year graduate students or to final-year undergraduate students there is no way to proceed smoothly without matrices and related concepts of linear algebra; their use is really essential. Our experience is that making some particular matrix tricks very familiar to students can substantially increase their insight into linear statistical models (and also multivariate statistical analysis). In matrix algebra, there are handy, sometimes even very simple “tricks” which simplify and clarify the treatment of a problem—both for the student and for the professor. Of course, the concept of a trick is not uniquely defined—by a trick we simply mean here a useful important handy result. In this book we collect together our Top Twenty favourite matrix tricks for linear statistical models.

This book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2022), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from 26 to 27 March 2022. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.

Statistical implicative analysis is a data analysis method created by Régis Gras almost thirty years ago which has a significant impact on a variety of areas ranging from pedagogical and psychological research to data mining. Statistical implicative analysis (SIA) provides a framework for evaluating the strength of implications; such implications are formed through common knowledge acquisition techniques in any learning process, human or artificial. This new concept has developed into a unifying methodology, and has generated a powerful convergence of thought between mathematicians, statisticians, psychologists, specialists in pedagogy and last, but not least, computer scientists specialized in data mining. This volume collects significant research contributions of several rather distinct disciplines that benefit from SIA. Contributions range from psychological and pedagogical research, bioinformatics, knowledge management, and data mining.

This book constitutes the thoroughly refereed proceedings of five international workshops held in Thessaloniki, Greece, in conjunction with the 26th International Conference on Advanced Information Systems Engineering, CAiSE 2014, in June 2014. The 24 full and eight short papers were carefully selected from 63 submissions. The five workshops were the First International Workshop on Advanced Probability and Statistics in Information Systems (APSIS), the First International Workshop on Advances in Services Design Based on the Notion of Capability, the Second International Workshop on Cognitive Aspects of Information Systems Engineering (COGNISE), the Third Workshop on New Generation Enterprise and Business Innovation Systems (NGEBIS), and the 4th International Workshop on Information Systems Security Engineering (WISSE).

Teaching Statistics in School Mathematics-Challenges for Teaching and Teacher Education results from the Joint ICMI/IASE Study Teaching Statistics in School Mathematics: Challenges for Teaching and Teacher Education. Oriented to analyse the teaching of statistics in school and to recommend improvements in the training of mathematics teachers to encourage success in preparing statistically literate students, the volume provides a picture of the current situation in both the teaching of school statistics and the pre-service education of mathematics teachers. A primary goal of Teaching Statistics in School Mathematics-Challenges for Teaching and Teacher Education is to describe the essential elements of statistics, teacher’s professional knowledge and their learning experiences. Moreover, a research agenda that invites new research, while building from current knowledge, is developed. Recommendations about strategies and materials, available to train prospective teachers in university and in-service teachers who have not been adequately prepared, are also accessible to the reader.