mODa 10 – Advances in Model-Oriented Design and Analysis: Proceedings of the 10th International Workshop in Model-Oriented Design and Analysis Held in Łagów Lubuski, Poland, June 10–14, 2013

Springer Science & Business Media
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This book collects the proceedings of the 10th Workshop on Model-Oriented Design and Analysis (mODa). A model-oriented view on the design of experiments, which is the unifying theme of all mODa meetings, assumes some knowledge of the form of the data-generating process and naturally leads to the so-called optimum experimental design. Its theory and practice have since become important in many scientific and technological fields, ranging from optimal designs for dynamic models in pharmacological research, to designs for industrial experimentation, to designs for simulation experiments in environmental risk management, to name but a few. The methodology has become even more important in recent years because of the increased speed of scientific developments, the complexity of the systems currently under investigation and the mounting pressure on businesses, industries and scientific researchers to reduce product and process development times. This increased competition requires ever increasing efficiency in experimentation, thus necessitating new statistical designs. This book presents a rich collection of carefully selected contributions ranging from statistical methodology to emerging applications. It primarily aims to provide an overview of recent advances and challenges in the field, especially in the context of new formulations, methods and state-of-the-art algorithms. The topics included in this volume will be of interest to all scientists and engineers and statisticians who conduct experiments.
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About the author

Dariusz Uciński is a Professor of Automatic Control and Robotics at the University of Zielona Góra, Poland. His major research areas include measurement optimization for spatio-temporal systems, optimum experimental design for dynamic systems, algorithmic optimal control, probabilistic robotics and parallel computing. He is the author of Optimal Measurement Methods for Distributed Parameter System Identification. Anthony C. Atkinson is an Emeritus Professor of Statistics at the London School of Economics. He has published extensively on robust statistical methods, generalized linear models, clinical trials, optimum experimental design, simulation and regression diagnostics. He has served as Editor of the Journal of the Royal Statistical Society, Series B. He is the author of Plots, Transformations, and Regression and co-author of Optimum Experimental Designs, Robust Diagnostic Regression Analysis and Exploring Multivariate Data with the Forward Search. Maciej Patan is a Reader in Electrical Engineering at the University of Zielona Góra, Poland. His research focuses on sensor scheduling for the optimal observation of spatio-temporal systems, optimum experimental design, clinical trials, scientific computing and mobile robotics. He is the author of Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems.
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Additional Information

Publisher
Springer Science & Business Media
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Published on
Mar 21, 2013
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Pages
249
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ISBN
9783319002187
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Language
English
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Genres
Computers / Mathematical & Statistical Software
Mathematics / Probability & Statistics / General
Mathematics / Probability & Statistics / Stochastic Processes
Medical / Biostatistics
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Content Protection
This content is DRM protected.
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Gareth James
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Anthony C. Atkinson
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