@article{Geliazkova2010,
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.},
author = {Geliazkova, Maya},
journal = {Serdica Journal of Computing},
keywords = {Spatial Mixture Models; CAR Model; ROC Analysis; Procedure; Bias; Variance; Mean Squared Error},
language = {eng},
number = {4},
pages = {417-434},
publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},
title = {A Bayesian Spatial Mixture Model for FMRI Analysis},
url = {http://eudml.org/doc/11397},
volume = {4},
year = {2010},
}
TY - JOUR
AU - Geliazkova, Maya
TI - A Bayesian Spatial Mixture Model for FMRI Analysis
JO - Serdica Journal of Computing
PY - 2010
PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences
VL - 4
IS - 4
SP - 417
EP - 434
AB - 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.
LA - eng
KW - Spatial Mixture Models; CAR Model; ROC Analysis; Procedure; Bias; Variance; Mean Squared Error
UR - http://eudml.org/doc/11397
ER -
