A Bayesian latent variable approach to functional principal components analysis with binary and count data

DOI10.1007/s10182-009-0113-6zbMath1331.62299OpenAlexW2003865934WikidataQ108929472 ScholiaQ108929472MaRDI QIDQ5963007

Angelika van der Linde

Publication date: 25 February 2016

Published in: AStA. Advances in Statistical Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10182-009-0113-6

zbMATH Keywords

splinesexponential familyfactor analysisworking observationslogistic PCAprobabilistic PCAvariational algorithm

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Bayesian inference (62F15)

Related Items (9)

Ordinal probit functional outcome regression with application to computer-use behavior in rhesus monkeys ⋮ Generalized functional additive mixed models ⋮ Generalized multilevel function‐on‐scalar regression and principal component analysis ⋮ Boosting functional response models for location, scale and shape with an application to bacterial competition ⋮ Nonnegative decomposition of functional count data ⋮ A note on density estimation for binary sequences ⋮ A general framework for functional regression modelling ⋮ Functional principal component analysis estimator for non-Gaussian data ⋮ Sparse logistic functional principal component analysis for binary data

Uses Software

• fda (R)

Cites Work

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• Variational Bayesian functional PCA
• Applied functional data analysis. Methods and case studies
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• Factor analysis with (mixed) observed and latent variables in the exponential family
• Some results on Tchebycheffian spline functions and stochastic processes
• Modelling Sparse Generalized Longitudinal Observations with Latent Gaussian Processes
• Principal component models for sparse functional data
• PCA-based dimension reduction for splines
• Variational Inference for Large-Scale Models of Discrete Choice
• Hierarchical models for assessing variability among functions

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