Four simultaneous component models for the analysis of multivariate time series from more than one subject to model intraindividual and interindividual differences

DOI10.1007/BF02296656zbMath1306.62507OpenAlexW2040805429MaRDI QIDQ2259553

Henk A. L. Kiers, Marieke E. Timmerman

Publication date: 4 March 2015

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02296656

zbMATH Keywords

three-way analysisexploratory longitudinal analysis

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Markov processes: estimation; hidden Markov models (62M05) Applications of statistics to psychology (62P15)

Related Items (12)

Estimating latent trends in multivariate longitudinal data via Parafac2 with functional and structural constraints ⋮ Regularized generalized canonical correlation analysis for multiblock or multigroup data analysis ⋮ Raw data maximum likelihood estimation for common principal component models: a state space approach ⋮ Simultaneous component analysis by means of Tucker3 ⋮ A systematic study into the factors that affect the predictive accuracy of multilevel VAR(1) models ⋮ Modeling differences in the dimensionality of multiblock data by means of clusterwise simultaneous component analysis ⋮ The special sign indeterminacy of the direct-fitting Parafac2 model: some implications, cautions, and recommendations for simultaneous component analysis ⋮ Using regression makes extraction of shared variation in multiple datasets easy ⋮ Batch process monitoring based on multilevel ICA-PCA ⋮ The SIMCLAS model: simultaneous analysis of coupled binary data matrices with noise heterogeneity between and within data blocks ⋮ Covariance pattern mixture models for the analysis of multivariate heterogeneous longitudinal data ⋮ Multilevel dimensionality-reduction methods

Uses Software

• ElemStatLearn
• PARAFAC

Cites Work

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• Hierarchical relations between methods for simultaneous component analysis and a technique for rotation to a simple simultaneous structure
• Development and validation of brief measures of positive and negative affect: The PANAS scales.
• The elements of statistical learning. Data mining, inference, and prediction

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