11 research outputs found
Parameter estimation and statistical test of geographically weighted bivariate Poisson inverse Gaussian regression models
No full textPropensity score matching of the gymnastics for diabetes mellitus using logistic regression
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Multivariate Spatial Autoregressive Model with Latent Variables: Application to Economic Growth Modeling
No full textResearch involving causal relationships among latent variables that generally use structural equation modeling (SEM) analysis and spatial data simultaneously has an unavoidable impact on using spatial SEM analysis. A region-based spatial SEM model was developed for cases that include spill-over effects across regions. Spatial weights in the inner model offered flexibility and were more informative than conventional techniques. Therefore, this research developed a model that involved multivariate data, latent variables, and spatial data, specifically spatial autoregressive, in the form of a multivariate spatial autoregressive model that involves latent variables (MSAR-VLs). This development integrated latent variable estimation, error distribution of the model, parameter estimation, spatial dependency testing, and partial parameter hypothesis testing, which used the weighted least squares (WLS), the properties of expected and variance value, maximum likelihood estimation (MLE) method, Lagrange multiplier (LM), and Wald test methods, respectively. The results of the MSAR-LVs model development were applied to the economic growth modeling for regencies in East Java, Indonesia. The research findings on developing the MSAR-LVs model are as follows: the error vector distribution in the MSAR-LVs model conformed to a normal distribution. Its mean value was the estimated vector of the exogenous variables. Meanwhile, the standard deviation was represented by a matrix derived from the Kronecker product between the inverse of a diagonal matrix containing the parameters of the outer model for the exogenous variables and the identity matrix. The parameter estimators did not have a closed-form solution; therefore, they were estimated using a numerical approximation approach based on the concentrated log-likelihood function. The estimator matrix that yielded the maximum log-likelihood value was selected. Under the null hypothesis, the LM and Wald test statistics conformed to a chi-square distribution with one degree of freedom. The other findings indicated that the economic growth model had a significant and negative spatial autoregressive coefficient, while the coefficient for the socio-demographic model was not significant. Additionally, human capital exerted a significant effect on economic growth, illustrating that each regency experienced a negative spill-over effect on economic growth from neighboring regencies, influenced by the present human capital in those regions
Modelling of the education quality of a high schools in Sumenep Regency using spatial structural equation modelling
No full textStructural Equation Modelling with Three Schemes Estimation of Score Factors on Partial Least Square (Case Study: The Quality Of Education Level SMA/MA in Sumenep Regency)
No full textA Spatial Error Model in Structural Equation for the Human Development Index Modeling
No full textSpatial regression, particularly the Spatial Error Model (SERM), was utilized in prior studies to analyze Human Development Index (HDI) modeling. However, the studies were unable to determine which dimension among the three defined by the UN and BPS had the significant impact on HDI, as they constructed models based on the indicators used for the interpretation of the dimensions. Therefore, a comprehensive analysis combining spatial regression and Structural Equation Modeling (SEM), known as spatial SEM, was deemed necessary. This is the reason the current study aimed to develop SERM-SEM modeling holistically. The model parameters were estimated using the Generalized Method of Moments (GMM). To assess spatial dependency, the Lagrange Multiplier (LM) method was employed, with a distinct model error distribution compared to the error distribution of the traditional spatial model. The result of the LM test development showed that, under the null hypothesis, the LM test statistics followed a distribution. The results of the SERM-SEM model development were applied to HDI modeling using data in 2022 with three latent variables, namely a Long and Healthy Life (LHL), Knowledge (Know_L), and a Decent Standard of Living (DLS) (based on UN standards). The assessment of the outer model in SEM was based on the loading factor values that exceed 0.5 and their significance. This evaluation aimed to identify indicators that effectively explained or measured latent variables, so it got the revised model in SEM. These indicators are LHL2 and LHL 4 to form LHL. DLS1 and DLS3 are indicators to make up DLS, and for Know_L, they are K2 and K3. The revised SEM model was analyzed using spatial. The results of the spatial dependency test showed that the HDI model significantly led to the SERM-SEM model. Knowledge and a decent standard of living variables significantly influence HDI
