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Functional Data Analysis for Sparse Longitudinal Data

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Figure

Table 1. Results for FPC Analysis Using Conditional Expectation (CE, corresponding to PACE) and Integration (IN) Methods for 100 Monte Carlo Runs With N = 100 Random Trajectories per Sample,
Figure 1. (a) Observed Individual Trajectories of 283 Sequences of CD4 Percentages and (b) Smooth Estimate of the Mean Function.
Figure 3. (a) Smooth Estimate of the Variance Function for CD4 Count Data and (b) Smooth Estimate of the Correlation Function, Eliminating the “Raw” Data Falling on the Diagonal.
Figure 7. Smooth Surface Estimates G (A.2) of the Covariance Func-  tions Obtained From the Complete Data (a) and From the Sparsified Data (b) for Yeast Cell Cycle Gene Expression Profiles.
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