Estimation Of Dependences Based On Empirical Data

Twenty-?ve years have passed since the publication of the Russian version of the book Estimation of Dependencies Based on Empirical Data (EDBED for short). Twen- ?ve years is a long period of time. During these years many things have happened. Looking back, one can see how rapidly life and technology have changed, and how slow and dif?cult it is to change the theoretical foundation of the technology and its philosophy. I pursued two goals writing this Afterword: to update the technical results presented in EDBED (the easy goal) and to describe a general picture of how the new ideas developed over these years (a much more dif?cult goal). The picture which I would like to present is a very personal (and therefore very biased) account of the development of one particular branch of science, Empirical - ference Science. Such accounts usually are not included in the content of technical publications. I have followed this rule in all of my previous books. But this time I would like to violate it for the following reasons. First of all, for me EDBED is the important milestone in the development of empirical inference theory and I would like to explain why. S- ond, during these years, there were a lot of discussions between supporters of the new 1 paradigm (now it is called the VC theory ) and the old one (classical statistics).

Within the last three decades, information modelling and knowledge bases have become essential subjects, not only for academic communities related to information systems and computer science, but also for businesses where information technology is applied. This book presents the proceedings of EJC 2014, the 24th International Conference on Information Modelling and Knowledge Bases, held in Kiel, Germany, in June 2014. The main themes of the conference were: conceptual modelling, including modelling and specification languages, domain specific conceptual modelling, and validating and communicating conceptual models; knowledge and information modelling and discovery, including knowledge representation and knowledge management, advanced data mining and analysis methods, as well as information recognition and information modelling; linguistics modelling; cross-cultural communication and social computing; environmental modelling; and multimedia data modelling and systems, which includes modelling multimedia information and knowledge, content-based multimedia data management, content-based multimedia retrieval as well as privacy and context enhancing technologies. This book will be of interest to all those who wish to keep abreast of new developments in the field of information modelling and knowledge bases.

About three years ago, an idea was discussed among some colleagues in the Division of Statistics at the University of California, Davis, as to the possibility of holding an international conference, focusing exclusively on nonparametric curve estimation. The fruition of this idea came about with the enthusiastic support of this project by Luc Devroye of McGill University, Canada, and Peter Robinson of the London School of Economics, UK. The response of colleagues, contacted to ascertain interest in participation in such a conference, was gratifying and made the effort involved worthwhile. Devroye and Robinson, together with this editor and George Metakides of the University of Patras, Greece and of the European Economic Communities, Brussels, formed the International Organizing Committee for a two week long Advanced Study Institute (ASI) sponsored by the Scientific Affairs Division of the North Atlantic Treaty Organization (NATO). The ASI was held on the Greek Island of Spetses between July 29 and August 10, 1990. Nonparametric functional estimation is a central topic in statistics, with applications in numerous substantive fields in mathematics, natural and social sciences, engineering and medicine. While there has been interest in nonparametric functional estimation for many years, this has grown of late, owing to increasing availability of large data sets and the ability to process them by means of improved computing facilities, along with the ability to display the results by means of sophisticated graphical procedures.

Himalaya, one of the global biodiversity hotspots, is the abode of a variety of flora and fauna. The Himalayan ecosystems have immense ecological, socioeconomic, and aesthetic significance as they provide a wide range of ecosystem services. The northwest Himalaya (NWH), covering three states of India viz., Uttarakhand, Himachal Pradesh, and Jammu and Kashmir, starts from the foothills of Shivaliks in the south and extends to the greater Himalaya in the north. This region is also the source of some of the major rivers of India. With the increase in population, the NWH ecosystems have been under threat due to deforestation, loss of biodiversity, expansion of agriculture and settlement, overexploitation of natural resources, habitat loss and fragmentation, poaching, mining, construction of roads and large dams, and unplanned tourism. The Himalaya being young and geotectonically active, remains inherently unstable, fragile, and prone to natural disasters. Climate change is also likely to impact the Himalayan cryosphere drastically. Recognizing the importance of the Himalaya, a National Mission for Sustaining the Himalayan Ecosystem, one of the eight missions under the National Action Plan on Climate Change (NAPCC) of Govt. of India, to conserve biodiversity, forest cover and other ecological values in the Himalayan region has been taken up. Spaceborne remote sensing with its ability to provide synoptic and repetitive coverage has emerged as a powerful tool for assessment and monitoring of the Himalayan resources and phenomena. Indian Institute of Remote Sensing, Dehradun has taken up a number of studies in the fields of geology, water resources, forestry, agriculture, urban settlement, etc., over the last decade. The book summarises the work carried out in different disciplines, illustrated with tables and figures and a host of relevant references. It is hoped that the book serves as an excellent reference of immense value to the students, researchers, professors, scientists, professionals, and decision makers working in the NWH region.

This book constitutes the refereed proceedings of the 13th International Conference on Engineering Applications of Neural Networks, EANN 2012, held in London, UK, in September 2012. The 49 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers describe the applications of neural networks and other computational intelligence approaches to intelligent transport, environmental engineering, computer security, civil engineering, financial forecasting, virtual learning environments, language interpretation, bioinformatics and general engineering.

"Contains over 2500 equations and exhaustively covers not only nonparametrics but also parametric, semiparametric, frequentist, Bayesian, bootstrap, adaptive, univariate, and multivariate statistical methods, as well as practical uses of Markov chain models."

Intercropping is an area of research for which there is a desperate need, both in developing countries where people are rapidly depleting scarce resources and still starving, and in developed countries, where more ecologically and economically sound ways of feeding ourselves must be developed. The only published guidelines for conducting such research and analyzing the data have been scattered about in various journal articles, many of which are hard to find. This book condenses these methods and will be immensely valuable to agricultural researchers and to the statisticians who help them design their experiments and interpret their results.

An up-to-date account of the theory and applications of linear models, for use as a textbook in statistics at graduate level as well as an accompanying text for other courses in which linear models play a part. The authors present a unified theory of inference from linear models with minimal assumptions, not only through least squares theory, but also using alternative methods of estimation and testing based on convex loss functions and general estimating equations. Highlights include: - a special emphasis on sensitivity analysis and model selection; - a chapter devoted to the analysis of categorical data based on logic, loglinear, and logistic regression models; - a chapter devoted to incomplete data sets; - an extensive appendix on matrix theory; - a chapter devoted to the analysis of categorical data based on a unified presentation of generalized linear models including GEE-methods for correlated response; - a chapter devoted to incomplete data sets including regression diagnostics to identify Non-MCAR-processes The material covered is thus invaluable not only to graduates, but also to researchers and consultants in statistics.