Spatial Mixture Models CAR Model ROC Analysis Procedure Bias Variance Mean Squared Error
Issue Date:
2010
Publisher:
Institute of Mathematics and Informatics Bulgarian Academy of Sciences
Citation:
Serdica Journal of Computing, Vol. 4, No 4, (2010), 417p-434p
Abstract:
We develop, implement and study a new Bayesian spatial mixture
model (BSMM). The proposed BSMM allows for spatial structure in the
binary activation indicators through a latent thresholded Gaussian Markov
random field. We develop a Gibbs (MCMC) sampler to perform posterior
inference on the model parameters, which then allows us to assess the posterior
probabilities of activation for each voxel. One purpose of this article
is to compare the HJ model and the BSMM in terms of receiver operating
characteristics (ROC) curves. Also we consider the accuracy of the spatial
mixture model and the BSMM for estimation of the size of the activation
region in terms of bias, variance and mean squared error. We perform a
simulation study to examine the aforementioned characteristics under a variety
of configurations of spatial mixture model and BSMM both as the size
of the region changes and as the magnitude of activation changes.