Abstract
High-dimensional data can be challenging to analyze. They are difficult to visualize, need extensive computer resources, and often require special statistical methodology. Fortunately, in many practical applications, high-dimensional data have most of their variation in a lower-dimensional space that can be found using dimension reduction techniques.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Ruppert, D. (2011). Factor Models and Principal Components. In: Statistics and Data Analysis for Financial Engineering. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7787-8_17
Download citation
DOI: https://doi.org/10.1007/978-1-4419-7787-8_17
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-7786-1
Online ISBN: 978-1-4419-7787-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)