About Haziq Jamil Haziq Jamil Research Specialist, Statistics Statistical methodology Bayesian computational statistics Latent variable models Structural equation models Item Response Theory Haziq Jamil is a Research Specialist at the King Abdullah University of Science and Technology. His research focuses on statistical theory, methods and computation, with a special inclination towards social science applications. Events Presented Events Oct 4 - Oct 10, 2026 Approximate Bayesian inference for structural equation models Haziq Jamil, Research Specialist, Statistics Oct 8, 12:00 - 13:00 B9 R2325 Laplace approximation Bayes estimators copulas R This talk reviews a fast approximate Bayesian SEM method that combines Laplace, variational Bayes, and Gaussian copula techniques to deliver near-MLE speed with MCMC-like inference, and is implemented in the R package INLAvaan. Oct 12 - Oct 18, 2025 Bias-Reduced Estimation of Structural Equations Models Haziq Jamil, Research Specialist, Statistics Oct 16, 12:00 - 13:00 B9 L2 R2325 This talk demonstrates that the reduced-bias M-estimation (RBM) framework is a computationally efficient and robust method for mitigating finite-sample bias in structural equation models, outperforming standard estimators, especially in small-sample contexts.
Approximate Bayesian inference for structural equation models Haziq Jamil, Research Specialist, Statistics Oct 8, 12:00 - 13:00 B9 R2325 Laplace approximation Bayes estimators copulas R This talk reviews a fast approximate Bayesian SEM method that combines Laplace, variational Bayes, and Gaussian copula techniques to deliver near-MLE speed with MCMC-like inference, and is implemented in the R package INLAvaan.
Bias-Reduced Estimation of Structural Equations Models Haziq Jamil, Research Specialist, Statistics Oct 16, 12:00 - 13:00 B9 L2 R2325 This talk demonstrates that the reduced-bias M-estimation (RBM) framework is a computationally efficient and robust method for mitigating finite-sample bias in structural equation models, outperforming standard estimators, especially in small-sample contexts.
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