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Zusatztext "The book deals with the necessary knowledge for understanding the theoretical and practical aspects regarding the common techniques of exploratory data analysis and modeling. For a better understanding! the underlying assumptions! mathematical formulations! and the algorithms involved by these techniques are presented. The authors made the text self-contained! the book being designed as a supplemental and referential resource for the practitioners dealing with this domain. The book also discusses a variety of practical topics more or less present in the literature."-Book Review by Florin Gorunescu! appearing in Zentralblatt MATH! 1306 Informationen zum Autor James Wu is 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. 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 over 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 . Klappentext Drawing on the authors' two decades of experience in applied modeling and data mining, this self-contained book presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, Zusammenfassung Drawing on the authors’ two decades of experience in applied modeling and data mining, this self-contained book presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, Inhaltsverzeichnis Introduction. Properties of Statistical Distributions. Important Matrix Relationships. Linear Modeling and Regression. Nonlinear Modeling. Time Series Analysis. Data Preparation and Variable Selection. Model Goodness Measures. Optimization Methods. Miscellaneous Topics. Appendices. Bibliography. Index. ...
