HPPCA is a hierarchical extension of PPCA that uses Gaussian processes to model within-subject dynamics in longitudinal data, outperforming standard PPCA and functional PCA in imputation under missingness and misspecification.
Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=
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Mixtures of convolutional measures on low-dimensional affine spaces admit unique identifiability in semi-parametric settings and posterior contraction rates under convex polytope support assumptions in a well-specified Bayesian regime.
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Hierarchical Probabilistic Principal Component Analysis of Longitudinal Data
HPPCA is a hierarchical extension of PPCA that uses Gaussian processes to model within-subject dynamics in longitudinal data, outperforming standard PPCA and functional PCA in imputation under missingness and misspecification.
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Learning Mixtures of Nonparametric and Convolutional Measures on Effectively Low-dimensional Affine Spaces
Mixtures of convolutional measures on low-dimensional affine spaces admit unique identifiability in semi-parametric settings and posterior contraction rates under convex polytope support assumptions in a well-specified Bayesian regime.