Computational Models For Life Sciences - CMLS '07: 2007 International Symposium on Computational Models of Life Sciences (AIP Conference Proceedings / ... / Mathematical and Statistical Physics)

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Computational Models For Life Sciences - CMLS '07: 2007 International Symposium on Computational Models of Life Sciences (AIP Conference Proceedings / ... / Mathematical and Statistical Physics) Overview

All papers have been peer-reviewed. This conference aims to bring biologists, medical and health-science researchers together with computational scientists to focus on problems at the frontier of computational life sciences. The conference proceedings consists of research papers addressing novel applications of computer, physical, engineering and mathematical models for solving modern challenging problems in life sciences. Topics include image analysis, computer vision, pattern analysis and classification, information visualization, signal processing, control theory, information theory, statistical analysis, numerical analysis, fractals and chaos, optimization, simulation and modeling, computational intelligence methods, machine learning, data mining, decision support systems, database integration and management.

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Introducing Multilevel Modeling (Introducing Statistical Methods series)

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Kreft and DeLeeuw's Introducing Multilevel Modeling was written to mercifully place only limited mathematical demands on readers. It includes an interesting and readable account of intercepts and slopes as outcomes. However, in common with most other texts, it fails to accessibly develop parallels between multilevel modeling and more widely understood procedures. Instead, the authors' intent seems to be to highlight differences, perhaps to make the distinctiveness, virtues, and limitations of multilevel modeling clear to the reader. Whatever the value of their approach, I find it much easier to understand multilevel analysis as an extension of multiple regression, a connection which Kreft and DeLeeuw do not develop.

Simple examples are presented throughout the book using MLn software. Models with more than two levels are not discussed. Growth models are mentioned in passing. The authors emphasize that the constraints imposed by multilevel analysis make it best suited to relatively simple models, but I think they over-emphasize this judgment. Models much more complex than those used in this text are usefully employed and widely reported in a variety of books and journal articles.

The authors also treat close calls with regard to statistical significance as of dubious value. The reason for this is not given, and I much prefer setting an alpha level and then sticking with it, rather than second-guessing results.

This is a brief text that addresses some issues of importance in the Frequently Asked Questions section at the end of the book. It is not a comprehensive reference, but that was not its intended purpose. It reads especially well the second time through, and gives a good deal of needed attention to centering, an important topic that is glossed over in most other multilevel texts.

Introducing Multilevel Modeling (Introducing Statistical Methods series) Overview

This is the first accessible and practical guide to using multilevel models in social research.

Multilevel approaches are becoming increasingly important in social, behavioural, and educational research and it is clear from recent developments that such models are seen as being more realistic, and potentially more revealing, than ordinary regression models.

While other books describe these multilevel models in considerable detail none focuses on the practical issues and potential problems of doing multilevel analyses that are covered in Introducing Multilevel Modeling.

The authors' approach is user-oriented and the formal mathematics and statistics are kept to a minimum. Other key fe

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• Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling
• Hierarchical Linear Models: Applications and Data Analysis Methods (Advanced Quantitative Techniques in the Social Sciences)
• Multilevel Modeling (Quantitative Applications in the Social Sciences)
• Multilevel Analysis: Techniques and Applications, Second Edition (Quantitative Methodology Series)
• HLM 6: Hierarchical Linear and Nonlinear Modeling

Visualizing Statistical Models and Concepts (Statistics: a Series of Textbooks and Monogrphs)

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Visualizing Statistical Models and Concepts (Statistics: a Series of Textbooks and Monogrphs) Overview

Examines classic algorithms, geometric diagrams, and mechanical principles for enhances visualization of statistical estimation procedures and mathematical concepts in physics, engineering, and computer programming.

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Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings ... Vision, Pattern Recognition, and Graphics)

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Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings ... Vision, Pattern Recognition, and Graphics) Overview

This book constitutes the refereed proceedings of the 12th International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2008 and the 7th International Workshop on Statistical Techniques in Pattern Recognition, SPR 2008, held jointly in Orlando, FL, USA, in December 2008 as a satellite event of the 19th International Conference of Pattern Recognition, ICPR 2008.

The 56 revised full papers and 42 revised poster papers presented together with the abstracts of 4 invited papers were carefully reviewed and selected from 175 submissions. The papers are organized in topical sections on graph-based methods, probabilistic and stochastic structural models for PR, image and video analysis, shape analysis, kernel methods, recognition and classification, applications, ensemble methods, feature selection, density estimation and clustering, computer vision and biometrics, pattern recognition and applications, pattern recognition, as well as feature selection and clustering.

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