Simultaneous non-parametric regressions of unbalanced longitudinal data
Computational Statistics & Data Analysis
Neural Computation
Functional principal components analysis by choice of norm
Journal of Multivariate Analysis
Proceedings of the 1998 conference on Advances in neural information processing systems II
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
On Bayesian principal component analysis
Computational Statistics & Data Analysis
Diagnostics for functional regression via residual processes
Computational Statistics & Data Analysis
Functional PLS logit regression model
Computational Statistics & Data Analysis
Robust forecasting of mortality and fertility rates: A functional data approach
Computational Statistics & Data Analysis
Principal component analysis of measures, with special emphasis on grain-size curves
Computational Statistics & Data Analysis
Classifying densities using functional regression trees: Applications in oceanology
Computational Statistics & Data Analysis
Editorial: Statistics for Functional Data
Computational Statistics & Data Analysis
Functional approach to the random mean of a compound Cox process
Computational Statistics
Computational considerations in functional principal component analysis
Computational Statistics
Functional estimation incorporating prior correlation information
Computational Statistics
An overview to modelling functional data
Computational Statistics
Forecasting binary longitudinal data by a functional PC-ARIMA model
Computational Statistics & Data Analysis
Bayesian inference on principal component analysis using reversible jump Markov chain Monte Carlo
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Modelling the mean of a doubly stochastic Poisson process by functional data analysis
Computational Statistics & Data Analysis
Blind separation of sparse sources using jeffrey’s inverse prior and the EM algorithm
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Dimensionality reduction when data are density functions
Computational Statistics & Data Analysis
Principal components for multivariate functional data
Computational Statistics & Data Analysis
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A Bayesian approach to analyze the modes of variation in a set of curves is suggested. It is based on a generative model thus allowing for noisy and sparse observations of curves. A Demmler-Reinsch(-type) basis is used to enforce smoothness of the latent ('eigen')functions. Inference, including estimation, error assessment and model choice, particularly the choice of the number of eigenfunctions and their degree of smoothness, is derived from a variational approximation of the posterior distribution. The proposed analysis is illustrated with simulated and real data.