Empirical Model Building: Data, Models, and Reality: 794 (Wiley Series in Probability and Statistics) - Tapa dura

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James R. Thompson, PhD, is Noah Harding Professor of Statistics at Rice University, where he was founding chairman of the Department of Statistics. A frequent consultant to government and the private sector, he holds adjunct professorships at the MD Anderson Cancer Center and the University of Texas School of Public Health. A Fellow of the American Statistical Association (ASA) and the International Statistical Institute, Dr. Thompson was the recipient of the Army Wilks Award for his work in defense-related data analysis and modeling, and was also the recipient of the ASA's Don Owen Award for his work in statistical process control. He is the author of Simulation: A Modeler's Approach and coauthor of Models for Investors in Real World Markets, both published by Wiley.

De la contraportada

Praise for the First Edition

"This . . . novel and highly stimulating book, which emphasizes solving real problems . . . should be widely read. It will have a positive and lasting effect on the teaching of modeling and statistics in general."
?Short Book Reviews

This new edition features developments and real-world examples that showcase essential empirical modeling techniques

Successful empirical model building is founded on the relationship between data and approximate representations of the real systems that generated that data. As a result, it is essential for researchers who construct these models to possess the special skills and techniques for producing results that are insightful, reliable, and useful. Empirical Model Building: Data, Models, and Reality, Second Edition presents a hands-on approach to the basic principles of empirical model building through a shrewd mixture of differential equations, computer-intensive methods, and data. The book outlines both classical and new approaches and incorporates numerous real-world statistical problems that illustrate modeling approaches that are applicable to a broad range of audiences, including applied statisticians and practicing engineers and scientists.

This new edition features developments and real-world examples that showcase essential empirical modeling techniques useful in various applications such as exploratory data analysis, diplomacy, combat, quality control, sampling, epidemiology, oncology, simulation-based solutions of differential equations, empirical investment strategies data-based modeling, and analysis of data sets of high dimensionality. The book continues to review models of growth and decay, systems where competition and interaction add to the complexity of the model while discussing both classical and non-classical data analysis methods. It now features further coverage of momentum-based investing practices and resampling techniques, showcasing their importance and expediency in the real world. The author provides applications of empirical modeling, such as computer modeling of the AIDS epidemic to explain why North America has most of the AIDS cases in the First World and data-based strategies that allow individual investors to build their own investment portfolios. Throughout the book, computer-based analysis is emphasized, and newly added and updated exercises allow readers to test their comprehension of the presented material.

Empirical Model Building, Second Edition is a suitable book for modeling courses at the upper-undergraduate and graduate levels. It is also an excellent reference for applied statisticians and researchers who carry out quantitative modeling in their everyday work.

De la solapa interior

Praise for the First Edition

This . . . novel and highly stimulating book, which emphasizes solving real problems . . . should be widely read. It will have a positive and lasting effect on the teaching of modeling and statistics in general.
--Short Book Reviews

This new edition features developments and real-world examples that showcase essential empirical modeling techniques

Successful empirical model building is founded on the relationship between data and approximate representations of the real systems that generated that data. As a result, it is essential for researchers who construct these models to possess the special skills and techniques for producing results that are insightful, reliable, and useful. Empirical Model Building: Data, Models, and Reality, Second Edition presents a hands-on approach to the basic principles of empirical model building through a shrewd mixture of differential equations, computer-intensive methods, and data. The book outlines both classical and new approaches and incorporates numerous real-world statistical problems that illustrate modeling approaches that are applicable to a broad range of audiences, including applied statisticians and practicing engineers and scientists.

This new edition features developments and real-world examples that showcase essential empirical modeling techniques useful in various applications such as exploratory data analysis, diplomacy, combat, quality control, sampling, epidemiology, oncology, simulation-based solutions of differential equations, empirical investment strategies data-based modeling, and analysis of data sets of high dimensionality. The book continues to review models of growth and decay, systems where competition and interaction add to the complexity of the model while discussing both classical and non-classical data analysis methods. It now features further coverage of momentum-based investing practices and resampling techniques, showcasing their importance and expediency in the real world. The author provides applications of empirical modeling, such as computer modeling of the AIDS epidemic to explain why North America has most of the AIDS cases in the First World and data-based strategies that allow individual investors to build their own investment portfolios. Throughout the book, computer-based analysis is emphasized, and newly added and updated exercises allow readers to test their comprehension of the presented material.

Empirical Model Building, Second Edition is a suitable book for modeling courses at the upper-undergraduate and graduate levels. It is also an excellent reference for applied statisticians and researchers who carry out quantitative modeling in their everyday work.