PACE: Principal Analysis by Conditional Expectation. PACE Package for Functional Data Analysis and Empirical Dynamics (MATLAB). PACE is a versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics in Matlab. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. PACE is useful for the analysis of data that have been generated by a sample of underlying (but usually not fully observed) random trajectories. It does not rely on pre-smoothing of trajectories, which is especially problematic when functional data are sparsely sampled. PACE provides options for linear and nonlinear functional regression and correlation, for Longitudinal Data Analysis, the analysis of stochastic processes from samples of realized trajectories, and for the analysis of underlying dynamics, through empirical differential equations.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Rha, Hyungmin; Kao, Ming-Hung; Pan, Rong: Bagging-enhanced sampling schedule for functional quadratic regression (2021)
- Jingjing Yang, Peng Ren: BFDA: A Matlab Toolbox for Bayesian Functional Data Analysis (2016) arXiv