Foundations of Predictive Analytics

ISBN-13: 9780367381684 / Angielski / Miękka / 2019 / 338 str.

James Wu; Stephen Coggeshall

Opis

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, risk and marketing analytics, and other areas. It explains the algorithmic details behind each technique, including underlying assumptions and mathematical formulations, and discusses a variety of practical topics that are frequently missing from similar texts. Software and examples are available at www.DataMinerXL.com

Spis treści

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.

O autorze

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.