| dc.identifier.uri | http://hdl.handle.net/1951/55695 | |
| dc.identifier.uri | http://hdl.handle.net/11401/72729 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.abstract | Structural Equation Modeling (SEM), also commonly referred to as path analysis in the absence of latent variables, is a powerful multivariate analysis approach to explore and to confirm causal relationships. It imposes a structure on the covariance matrix and the imposed structure is subsequently validated by the data. In recent years, SEM has been extended to analyze autoregressive moving average (ARMA) time series data assuming time-constant path coefficients. The mechanism of ARMA-based SEM makes it the ideal procedure for the analysis of directional brain functional pathways based on the multi-subject, multivariate time series data generated through the functional magnetic resonance imaging (fMRI) studies. However the time-constant path coefficient assumption is unrealistic and overly restrictive. In this work, based on converting the overall SEM to the sectional SEM approach for vector ARMA(p, q) time series, we extend the ARMA-based SEM to allow time-varying coefficients (TVC). The statistical inference framework based on the maximum likelihood method is derived and the advantage of the novel TVC SEM approach is demonstrated through simulation studies. In addition, we also applied the new method to examine the brain visual-attention pathway based on an fMRI experiment conducted at the Brookhaven National Laboratory. Other than brain functional pathways studies, the TVC SEM method can be readily applied to analyze other longitudinal data such as the financial time series. | |
| dcterms.available | 2012-05-15T18:07:57Z | |
| dcterms.available | 2015-04-24T14:53:24Z | |
| dcterms.contributor | Stephen J. Finch | en_US |
| dcterms.contributor | Feinberg, Eugene | en_US |
| dcterms.contributor | Haipeng Xing | en_US |
| dcterms.contributor | Ellen Li. | en_US |
| dcterms.creator | Zhang, Tianyi | |
| dcterms.dateAccepted | 2012-05-15T18:07:57Z | |
| dcterms.dateAccepted | 2015-04-24T14:53:24Z | |
| dcterms.dateSubmitted | 2012-05-15T18:07:57Z | |
| dcterms.dateSubmitted | 2015-04-24T14:53:24Z | |
| dcterms.description | Department of Applied Mathematics and Statistics | en_US |
| dcterms.format | Monograph | |
| dcterms.format | Application/PDF | en_US |
| dcterms.identifier | http://hdl.handle.net/1951/55695 | |
| dcterms.identifier | Zhang_grad.sunysb_0771E_10062.pdf | en_US |
| dcterms.identifier | http://hdl.handle.net/11401/72729 | |
| dcterms.issued | 2010-05-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2012-05-15T18:07:57Z (GMT). No. of bitstreams: 1
Zhang_grad.sunysb_0771E_10062.pdf: 868938 bytes, checksum: f9749086129d622daa349a2fa26a6b9c (MD5)
Previous issue date: 1 | en |
| dcterms.provenance | Made available in DSpace on 2015-04-24T14:53:24Z (GMT). No. of bitstreams: 3
Zhang_grad.sunysb_0771E_10062.pdf.jpg: 1894 bytes, checksum: a6009c46e6ec8251b348085684cba80d (MD5)
Zhang_grad.sunysb_0771E_10062.pdf.txt: 141597 bytes, checksum: 2a3915711c698d3c8718bdacc9629124 (MD5)
Zhang_grad.sunysb_0771E_10062.pdf: 868938 bytes, checksum: f9749086129d622daa349a2fa26a6b9c (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | ARMA Time Series, fMRI Data, Structural Equation Modeling, Time Varying Coefficients | |
| dcterms.subject | Statistics | |
| dcterms.title | Structural Equation Modeling with Time Series Data | |
| dcterms.type | Dissertation | |
