Empirical Model Building Data, Models, and Reality

This book presents a hands-on approach to the basic principles of empirical model building through the shrewd mixture of differential equations, computer-intensive methods, and data in a single-volume. It includes a series of real-world statistical problems illustrating modeling skills and techniques that are applicable to a broad range of audiences from applied statisticians to practicing MBAs. It covers models of growth and decay, systems where competition and interaction add to the complexity of the model, and discusses both classical and non-classical data analysis methods, alongside an extended list of more than twenty essential topics. The author also includes numerous exercises, an emphasis on computational finance and Bayesian techniques, and timely discussions of epidemics, quality control, and chaos in a dynamic world.

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.