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Activity Number: 545
Type: Contributed
Date/Time: Thursday, August 2, 2007 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #310122
Title: Alternative Penalty Functions for Principal Components
Author(s): Trevor Park*+
Companies: University of Florida
Address: 103 Griffin/Floyd Hall, Gainesville, FL, 32611,
Keywords: penalized likelihood ; functional data ; rotation
Abstract:

The high variability inherent in principal component analysis, particularly as applied to functional data, has spurred development of much methodology for rotating or regularizing the components to improve interpretation. We consider methods for constructing and applying penalty functions designed for special types of data and embracing various notions of interpretability. Effectiveness of specially-designed penalty functions is illustrated in the context of penalized likelihood principal component rotation, and some algorithms are described. Examples of application to data sets, including functional data sets, are offered.

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Revised September, 2007