A5094272388- Statistical Methods and Inference
- Soil Geostatistics and Mapping
- Spatial and Panel Data Analysis
- Statistical Methods and Bayesian Inference
- Bayesian Methods and Mixture Models
University of Wollongong
2024
Efficient estimation methods for simultaneous autoregressive (SAR) models with missing data in the response variable have been well explored literature. A common practice is introducing measurement error into SAR to separate noise component from spatial process. However, prior studies not considered incorporating data. Maximum likelihood such models, especially large datasets, poses significant computational challenges. This paper proposes an efficient likelihood-based method, marginal...
Efficient estimation methods for simultaneous autoregressive (SAR) models with missing data in the response variable have been well-developed literature. It is common practice to introduce a measurement error into SAR models. The serves distinguish noise component from spatial process. However, previous literature has not considered adding data. maximum likelihood such large datasets challenging and computationally expensive. This paper proposes two efficient likelihood-based methods:...
The spatial error model (SEM) is a type of simultaneous autoregressive (SAR) for analysing spatially correlated data. Markov chain Monte Carlo (MCMC) one the most widely used Bayesian methods estimating SEM, but it has significant limitations when comes to handling missing data in response variable due its high computational cost. Variational Bayes (VB) approximation offers an alternative solution this problem. Two VB-based algorithms employing Gaussian variational with factor covariance...