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

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  • Marieke Timmerman
  • Henk Kiers

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  • Marieke Timmerman & Henk Kiers, 2003. "Four simultaneous component models for the analysis of multivariate time series from more than one subject to model intraindividual and interindividual differences," Psychometrika, Springer;The Psychometric Society, vol. 68(1), pages 105-121, March.
  • Handle: RePEc:spr:psycho:v:68:y:2003:i:1:p:105-121
    DOI: 10.1007/BF02296656
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    References listed on IDEAS

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    1. Timmerman, Marieke E. & Kiers, Henk A. L., 2002. "Three-way component analysis with smoothness constraints," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 447-470, September.
    2. Henry Kaiser, 1958. "The varimax criterion for analytic rotation in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 23(3), pages 187-200, September.
    3. Norman Cliff, 1966. "Orthogonal rotation to congruence," Psychometrika, Springer;The Psychometric Society, vol. 31(1), pages 33-42, March.
    4. Michael W. Browne & Robert Cudeck, 1992. "Alternative Ways of Assessing Model Fit," Sociological Methods & Research, , vol. 21(2), pages 230-258, November.
    5. T. Anderson, 1963. "The use of factor analysis in the statistical analysis of multiple time series," Psychometrika, Springer;The Psychometric Society, vol. 28(1), pages 1-25, March.
    6. Peter Molenaar, 1985. "A dynamic factor model for the analysis of multivariate time series," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 181-202, June.
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    Citations

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    Cited by:

    1. Nathaniel Helwig, 2013. "The Special Sign Indeterminacy of the Direct-Fitting Parafac2 Model: Some Implications, Cautions, and Recommendations for Simultaneous Component Analysis," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 725-739, October.
    2. Fei Gu & Hao Wu, 2016. "Raw Data Maximum Likelihood Estimation for Common Principal Component Models: A State Space Approach," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 751-773, September.
    3. Kim De Roover & Eva Ceulemans & Marieke Timmerman & John Nezlek & Patrick Onghena, 2013. "Modeling Differences in the Dimensionality of Multiblock Data by Means of Clusterwise Simultaneous Component Analysis," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 648-668, October.
    4. Sandra Waaijenborg & Oksana Korobko & Ko Willems van Dijk & Mirjam Lips & Thomas Hankemeier & Tom F Wilderjans & Age K Smilde & Johan A Westerhuis, 2018. "Fusing metabolomics data sets with heterogeneous measurement errors," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-19, April.
    5. Ginette Lafit & Kristof Meers & Eva Ceulemans, 2022. "A Systematic Study into the Factors that Affect the Predictive Accuracy of Multilevel VAR(1) Models," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 432-476, June.
    6. Alwin Stegeman, 2018. "Simultaneous Component Analysis by Means of Tucker3," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 21-47, March.
    7. Pietro Lovaglio & Giorgio Vittadini, 2013. "Multilevel dimensionality-reduction methods," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(2), pages 183-207, June.
    8. Tom Wilderjans & E. Ceulemans & I. Mechelen, 2012. "The SIMCLAS Model: Simultaneous Analysis of Coupled Binary Data Matrices with Noise Heterogeneity Between and Within Data Blocks," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 724-740, October.
    9. Tenenhaus, Arthur & Tenenhaus, Michel, 2014. "Regularized generalized canonical correlation analysis for multiblock or multigroup data analysis," European Journal of Operational Research, Elsevier, vol. 238(2), pages 391-403.

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