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Factor Models and Principal Components

  • David Ruppert
  • David S. Matteson
Part of the Springer Texts in Statistics book series (STS)

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. There are many methods designed for dimension reduction, and in this chapter we will study two closely related techniques, factor analysis and principal components analysis, often called PCA.

Keywords

Factor Model Yield Curve Excess Return Asset Return Dividend Yield 
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.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • David Ruppert
    • 1
  • David S. Matteson
    • 2
  1. 1.Department of Statistical Science and School of ORIECornell UniversityIthacaUSA
  2. 2.Department of Statistical Science Department of Social StatisticsCornell UniversityIthacaUSA

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