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Dynamic GSCA (Generalized Structured Component Analysis) with Applications to the Analysis of Effective Connectivity in Functional Neuroimaging Data

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  • Kwanghee Jung
  • Yoshio Takane
  • Heungsun Hwang
  • Todd Woodward

Abstract

We propose a new method of structural equation modeling (SEM) for longitudinal and time series data, named Dynamic GSCA (Generalized Structured Component Analysis). The proposed method extends the original GSCA by incorporating a multivariate autoregressive model to account for the dynamic nature of data taken over time. Dynamic GSCA also incorporates direct and modulating effects of input variables on specific latent variables and on connections between latent variables, respectively. An alternating least square (ALS) algorithm is developed for parameter estimation. An improved bootstrap method called a modified moving block bootstrap method is used to assess reliability of parameter estimates, which deals with time dependence between consecutive observations effectively. We analyze synthetic and real data to illustrate the feasibility of the proposed method. Copyright The Psychometric Society 2012

Suggested Citation

  • Kwanghee Jung & Yoshio Takane & Heungsun Hwang & Todd Woodward, 2012. "Dynamic GSCA (Generalized Structured Component Analysis) with Applications to the Analysis of Effective Connectivity in Functional Neuroimaging Data," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 827-848, October.
    • Handle: RePEc:spr:psycho:v:77:y:2012:i:4:p:827-848
    DOI: 10.1007/s11336-012-9284-2
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    References listed on IDEAS

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    1. Forrest Young & Jan Leeuw & Yoshio Takane, 1976. "Regression with qualitative and quantitative variables: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 41(4), pages 505-529, December.
    2. Heungsun Hwang & Yoshio Takane, 2004. "Generalized structured component analysis," Psychometrika, Springer;The Psychometric Society, vol. 69(1), pages 81-99, March.
    3. Heungsun Hwang & Wayne Desarbo & Yoshio Takane, 2007. "Fuzzy Clusterwise Generalized Structured Component Analysis," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 181-198, June.
    4. Heungsun Hwang & Moon-Ho Ho & Jonathan Lee, 2010. "Generalized Structured Component Analysis with Latent Interactions," Psychometrika, Springer;The Psychometric Society, vol. 75(2), pages 228-242, June.
    5. Jan Leeuw & Forrest Young & Yoshio Takane, 1976. "Additive structure in qualitative data: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 41(4), pages 471-503, December.
    6. Jos Berge & Klaas Nevels, 1977. "A general solution to Mosier's oblique procrustes problem," Psychometrika, Springer;The Psychometric Society, vol. 42(4), pages 593-600, December.
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    Cited by:

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    6. Zhou, Lixing & Takane, Yoshio & Hwang, Heungsun, 2016. "Dynamic GSCANO (Generalized Structured Canonical Correlation Analysis) with applications to the analysis of effective connectivity in functional neuroimaging data," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 93-109.

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