Foundations of Predictive Analytics
Published on 15. February 2012
338 pages
978-1-040-16243-9 (ISBN)
Description
Drawing on the authors' two decades of experience in applied modeling and data mining, 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, and other areas. It also discusses a variety
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
File size
1,78 MB
ISBN-13
978-1-040-16243-9 (9781040162439)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
James Wu | Stephen Coggeshall
Foundations of Predictive Analytics
Book
09/2019
1st Edition
Chapman & Hall/CRC
€94.30
Shipment within 15-20 days
James Wu | Stephen Coggeshall
Foundations of Predictive Analytics
Book
02/2012
1st Edition
Chapman & Hall/CRC
€146.30
Shipment within 15-20 days
Persons
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.
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.
Content
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.
