Empirical Model Building: Data, Models, and Reality (Google eBook)
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.
The book continues to review models of growth and decay, systems where competition and interaction add to the complextiy of the model while discussing both classical and non-classical data analysis methods. This Second Edition 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.
What people are saying - Write a review
Data Models and Reality 2 Models of Competition Survival and Combat
Data Models and Reality 3 Epidemics
Data Models and Reality 4 Bootstrapping
Data Models and Reality 5 Monte Carlo Solutions of Differential Equations
Data Models and Reality 6 SIMEST SIMDAT and Pseudoreality
Data Models and Reality 7 Exploratory Data Analysis
Data Models and Reality 8 Noise Killing Chaos
Data Models and Reality 10 Multivariate and Robust Procedures in Statistical Process Control
Data Models and Reality 11 Considerations for Optimization and Estimation in the Real Noisy World
Data Models and Reality 12 Utility and Group Preference
Data Models and Reality 13 A Primer in Sampling
Data Models and Reality 14 Stock Market Strategies Based on Data versus Strategies Based on Ideology
Data Models and Reality Appendix A A Brief Introduction to Probability and Statistics
Data Models and Reality Appendix B Statistical Tables
Data Models and Reality 9 A Primer in Bayesian Data Analysis