ID Analytics Announces Publication of Foundation of Predictive Analytics
March 6, 2012
Company’s CTO Co-Authors Book on Applied Modeling and Data Mining
SAN DIEGO, CA – ID Analytics, a leader in consumer risk management, today announced a new book co-authored by the Company’s Chief Technology Officer, Dr. Stephen Coggeshall. Written by Dr. James Wu and Dr. Coggeshall, the book titled Foundations of Predictive Analytics, draws on the authors’ two decades of experience in applied modeling and data mining to offer the reader a comprehensive overview of applied analytics.
Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for many practical applications such as consumer behavior modeling, risk and marketing analytics, stock forecasting, and many other applied areas. In addition to the foundational theory and methodologies, it discusses a variety of practical topics that are frequently missing from similar texts.
The book begins with the statistical and linear algebra/matrix foundation of modeling methods, from distributions to cumulant and copula functions to Cornish–Fisher expansion and other useful but hard-to-find statistical techniques. It then describes common and unusual linear methods as well as popular nonlinear modeling approaches, including additive models, trees, support vector machines, fuzzy systems, clustering, naïve Bayes, and neural nets. The authors go on to cover methodologies used in time series and forecasting such as ARIMA, GARCH, and survival analysis. They also present a wide variety of optimization techniques and explore several unusual topics, such as Dempster–Shafer theory.
Highlights of the book include:
- Contains all of the key elements required for statistical modeling and predictive analytics
- Covers a wide range of important but difficult-to-find topics
- Gives a step-by-step mathematical derivation of each technique, from the underlying assumptions to final conclusion
- Discusses the practical aspects of modeling and predicting, with many examples from consumer behavior modeling
- Provides software and examples at www.DataMinerXL.com
An in-depth collection of the most important fundamental material on predictive analytics, this self-contained book provides the necessary information for understanding various techniques for exploratory data analysis and modeling. It explains the algorithmic details behind each technique (including underlying assumptions and mathematical formulations) and shows how to prepare and encode data, select variables, use a variety of model goodness measures, normalize odds, and perform reject inference.
The book’s website at www.DataMinerXL.com offers the DataMinerXL software for building predictive models. The site also includes more examples and information on modeling.
Foundations of Predictive Analytics is a volume in the Chapman & Hall/CRC Data Mining and Knowledge Discovery Series edited by Vipin Kumar, University of Minnesota, Minneapolis.
The book is available for purchase at www.amazon.com.
About the Authors
Stephen Coggeshall is the Chief Technology Officer of ID Analytics. He previously worked at Los Alamos Computational Group, Morgan Stanley, HNC Software, CASA, and Los Alamos National Laboratory. During his more than 20 year career, Dr. Coggeshall has helped teams of scientists develop practical solutions to difficult business problems using advanced analytics. He earned a PhD from the University of Illinois and was named 2008 Technology Executive of the Year by the San Diego Business Journal.
James Wu is currently a Fixed Income Quant with extensive expertise in a wide variety of applied analytical solutions in consumer behavior modeling and financial engineering. He previously worked at ID Analytics, Morgan Stanley, JPMorgan Chase, Los Alamos Computational Group and CASA. He earned a PhD from the University of Idaho.
About ID Analytics, Inc.
ID Analytics is transforming consumer risk management with patented analytics, proven expertise, and real-time insight into consumer behavior. By combining proprietary data from the ID Network®—one of the nation’s largest networks of cross-industry behavioral data—with advanced science, ID Analytics provides unprecedented visibility into identity risk and creditworthiness. Every day, the largest U.S. companies and critical government agencies rely on ID Analytics to make risk-based decisions that enhance revenue, reduce fraud, drive cost savings, and protect consumers. Please visit us on the web at www.idanalytics.com.
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