Dynamic Bayesian network for robust latent variable modeling and fault classification

2020 ◽  
Vol 89 ◽  
pp. 103475 ◽  
Author(s):  
Junhua Zheng ◽  
Jinlin Zhu ◽  
Guangjie Chen ◽  
Zhihuan Song ◽  
Zhiqiang Ge
Keyword(s):  
Bayesian Network ◽  
Latent Variable ◽  
Dynamic Bayesian Network ◽  
Fault Classification ◽  
Latent Variable Modeling

Progress in Latent Variable Modeling

2004 ◽  
Vol 49 (2) ◽  
pp. 204-204
Author(s):  
Alexander von Eye
Keyword(s):  
Latent Variable ◽  
Latent Variable Modeling

Neuroimaging of Individual Differences: A Latent Variable Modeling Perspective

2018 ◽  
Author(s):  
Shelly Renee Cooper ◽  
Joshua James Jackson ◽  
Deanna Barch ◽  
Todd Samuel Braver
Keyword(s):  
Individual Differences ◽  
Individual Difference ◽  
Latent Variable ◽  
Structural Equation ◽  
Behavioral Measure ◽  
Equation Modeling ◽  
Latent Variable Modeling ◽  
Psychometric Theory ◽  
Human Connectome Project ◽  
Neuroimaging Data

Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.

Evaluation of Second- and Third-Level Variance Proportions in Multilevel Designs With Completely Observed Populations: A Note on a Latent Variable Modeling Procedure

2021 ◽  
pp. 001316442110086
Author(s):  
Tenko Raykov ◽  
Natalja Menold ◽  
Jane Leer
Keyword(s):  
Latent Variable ◽  
Large Scale ◽  
Present Note ◽  
Interval Estimation ◽  
Psychological Research ◽  
Latent Variable Modeling ◽  
Model Choice ◽  
Modeling Methodology ◽  
Level 3 ◽  
Level 2

Two- and three-level designs in educational and psychological research can involve entire populations of Level-3 and possibly Level-2 units, such as schools and educational districts nested within a given state, or neighborhoods and counties in a state. Such a design is of increasing relevance in empirical research owing to the growing popularity of large-scale studies in these and cognate disciplines. The present note discusses a readily applicable procedure for point-and-interval estimation of the proportions of second- and third-level variances in such multilevel settings, which may also be employed in model choice considerations regarding ensuing analyses for response variables of interest. The method is developed within the framework of the latent variable modeling methodology, is readily utilized with widely used software, and is illustrated with an example.

Sign in / Sign up

Export Citation Format

Share Document