Bayesian Sparse Covariance Structure Analysis for Correlated Count Data
This work addresses the challenge of modeling correlated count data in spatial statistics, specifically for crime analysis, but appears incremental as it adapts existing Bayesian and LASSO methods to a new data type.
The authors tackled the problem of analyzing correlated count data by proposing a Bayesian Graphical LASSO model, applying it to spatial crime data to estimate sparse inverse covariance and partial correlation coefficients for latent variables representing crime risks.
In this paper, we propose a Bayesian Graphical LASSO for correlated countable data and apply it to spatial crime data. In the proposed model, we assume a Gaussian Graphical Model for the latent variables which dominate the potential risks of crimes. To evaluate the proposed model, we determine optimal hyperparameters which represent samples better. We apply the proposed model for estimation of the sparse inverse covariance of the latent variable and evaluate the partial correlation coefficients. Finally, we illustrate the results on crime spots data and consider the estimated latent variables and the partial correlation coefficients of the sparse inverse covariance.