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Activity Number: 271
Type: Contributed
Date/Time: Tuesday, August 5, 2008 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #301781
Title: Improved Covariance Inversion and Derivative Estimation for Sparse and Irregularly Observed Functional Data
Author(s): Bitao Liu*+ and Hans G. Müller
Companies: University of California, Davis and University of California, Davis
Address: Department of Statistics, Davis, CA, 95616,
Keywords: Functional Data Analysis ; Longitudinal Data ; Inverse Covariance ; Derivatives
Abstract:

We discuss an improved functional principal component analysis approach to represent sparsely observed longitudinal trajectories. It is based on advances in estimating the error variance and shrinkage which in turn leads to better estimates of inverses of covariance matrices of the underlying stochastic process. The gains of improved covariance inversion are demonstrated for the difficult problem of estimating derivatives from sparse and irregularly observed data. We present some theoretical results for this problem, simulation comparisons and data illustrations.

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