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The association between a persons physical activity and various health outcomes is an area of active research. The National Health and Nutrition Examination Survey (NHANES) data provide a valuable resource for studying these associations. NHANES accelerometry data has been used by many to measure individuals activity levels. A common approach for analyzing accelerometry data is functional principal component analysis (FPCA). The first part of the paper uses Poisson FPCA (PFPCA), Gaussian FPCA (GFPCA), and nonnegative and regularized function decomposition (NARFD) to extract features from the count-valued NHANES accelerometry data. The second part of the paper compares logistic regression, random forests, and AdaBoost models based on GFPCA, NARFD, or PFPCA scores in the context of mortality prediction. The results show that Poisson FPCA is the best FPCA model for the inference of accelerometry data, and the AdaBoost model based on Poisson FPCA scores gives the best mortality prediction results.
Functional principal component analysis (FPCA) has been widely used to capture major modes of variation and reduce dimensions in functional data analysis. However, standard FPCA based on the sample covariance estimator does not work well in the prese
Motivated by the analysis of high-dimensional neuroimaging signals located over the cortical surface, we introduce a novel Principal Component Analysis technique that can handle functional data located over a two-dimensional manifold. For this purpos
We show how to efficiently project a vector onto the top principal components of a matrix, without explicitly computing these components. Specifically, we introduce an iterative algorithm that provably computes the projection using few calls to any b
Functional principal component analysis (FPCA) could become invalid when data involve non-Gaussian features. Therefore, we aim to develop a general FPCA method to adapt to such non-Gaussian cases. A Kenalls $tau$ function, which possesses identical e
Functional binary datasets occur frequently in real practice, whereas discrete characteristics of the data can bring challenges to model estimation. In this paper, we propose a sparse logistic functional principal component analysis (SLFPCA) method t