Shaobo Jin
Biographic Data
| ID | 9667832 |
|---|---|
| NAME | Shaobo Jin |
| GIVEN NAMES | Shaobo |
| FAMILY NAME | Jin |
| SIGNATURE | JIN S |
| AFFILIATIONS | Uppsala University |
| ORCID | 0000-0001-6538-3477 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
On Inconsistency of the Overidentification Test for the Model-implied Instrumental Variable Approach
In the context of structural equation modeling, the model-implied instrumental variable (MIIV) approach has been shown to be more robust against model misspecification than the systemwide approaches (e.g., maximum likelihood and least squares). Besides the goodness-of-fit tests that test the fit of the entire hypothesized covariance structure, the overidentification tests for MIIV can be used to test model specification on an equation-by-equation…
Estimating and Testing Random Intercept Multilevel Structural Equation Models with Model Implied Instrumental Variables
This study develops a new limited information estimator for random intercept Multilevel Structural Equation Models (MSEM). It is based on the Model Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) estimator, which has been shown to be an excellent alternative or supplement to maximum likelihood (ML) in SEMs (Bollen, 1996 Bollen, K. A. (1996). An alternative two stage least squares (2SLS) estimator for latent variable equations. P…
Robust nonlinear structural equation modeling with interaction between exogenous and endogenous latent variables
A handful of studies have been devoted to nonlinear structural equation modeling (SEM) in the literature. However, they generally overlooked the interactions among exogenous and endogenous latent variables and the interactions among endogenous latent variables. In this study, we propose a maximum likelihood approach for a nonlinear SEM model that incorporates such overlooked interactions. We also propose a two-stage pseudo maximum likelihood appr…
A Semiparametric Approach for Structural Equation Modeling with Ordinal Data
There is currently a lack of methods for non-linear structural equation modeling (NSEM) for non-parametric relationships between latent variables when data are ordinal. To this end, a semiparametric approach for flexible NSEMs without parametric forms is developed for ordinal data. An indirect application of a finite mixture of structural equation models (SEMM) is employed for modeling the conditional expected mean of endogenous latent variables.…
A Marginal Maximum Likelihood Approach for Extended Quadratic Structural Equation Modeling with Ordinal Data
The literature on non-linear structural equation modeling is plentiful. Despite this fact, few studies consider interactions between exogenous and endogenous latent variables. Further, it is well known that treating ordinal data as continuous produces bias, a problem that is enhanced when non-linear relationships between latent variables are incorporated. A marginal maximum likelihood-based approach is proposed in order to fit a non-linear struct…
No prominent works on this page.
A Marginal Maximum Likelihood Approach for Extended Quadratic Structural Equation Modeling with Ordinal Data
The literature on non-linear structural equation modeling is plentiful. Despite this fact, few studies consider interactions between exogenous and endogenous latent variables. Further, it is well known that treating ordinal data as continuous produces bias, a problem that is enhanced when non-linear relationships between latent variables are incorporated. A marginal maximum likelihood-based approach is proposed in order to fit a non-linear struct…
Robust nonlinear structural equation modeling with interaction between exogenous and endogenous latent variables
A handful of studies have been devoted to nonlinear structural equation modeling (SEM) in the literature. However, they generally overlooked the interactions among exogenous and endogenous latent variables and the interactions among endogenous latent variables. In this study, we propose a maximum likelihood approach for a nonlinear SEM model that incorporates such overlooked interactions. We also propose a two-stage pseudo maximum likelihood appr…
A Semiparametric Approach for Structural Equation Modeling with Ordinal Data
There is currently a lack of methods for non-linear structural equation modeling (NSEM) for non-parametric relationships between latent variables when data are ordinal. To this end, a semiparametric approach for flexible NSEMs without parametric forms is developed for ordinal data. An indirect application of a finite mixture of structural equation models (SEMM) is employed for modeling the conditional expected mean of endogenous latent variables.…
Estimating and Testing Random Intercept Multilevel Structural Equation Models with Model Implied Instrumental Variables
This study develops a new limited information estimator for random intercept Multilevel Structural Equation Models (MSEM). It is based on the Model Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) estimator, which has been shown to be an excellent alternative or supplement to maximum likelihood (ML) in SEMs (Bollen, 1996 Bollen, K. A. (1996). An alternative two stage least squares (2SLS) estimator for latent variable equations. P…
On Inconsistency of the Overidentification Test for the Model-implied Instrumental Variable Approach
In the context of structural equation modeling, the model-implied instrumental variable (MIIV) approach has been shown to be more robust against model misspecification than the systemwide approaches (e.g., maximum likelihood and least squares). Besides the goodness-of-fit tests that test the fit of the entire hypothesized covariance structure, the overidentification tests for MIIV can be used to test model specification on an equation-by-equation…
Applied Mathematics (5 works) · Econometrics (5 works) · Mathematics (5 works) · Statistics (5 works) · Structural equation modeling (5 works) · Advanced Statistical Modeling Techniques (3 works) · Latent variable (3 works) · Maximum likelihood (3 works) · Statistical Methods and Bayesian Inference (3 works) · Instrumental variable (2 works)