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

Kwanghee Jung (), Yoshio Takane (), Heungsun Hwang () and Todd Woodward ()

Psychometrika, 2012, vol. 77, issue 4, 827-848

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

Keywords: generalized structured component analysis (GSCA); structural equation modeling (SEM); longitudinal and time series data; alternating least squares (ALS) algorithm; a modified moving block bootstrap method; functional neuroimaging; effective connectivity (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1007/s11336-012-9284-2

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