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10.1111/biom.12085 http://scihub22266oqcxt.onion/10.1111/biom.12085 C3864982!3864982!24128143     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biometrics 2013 ; 69 (4): ä Nephropedia Template TP {{tp|p=24128143|t=2013. Bayesian spatial transformation models with applications in neuroimaging data |pdf=|usr=}}{{24128143}} gab.com Text Bayesian spatial transformation models with applications in neuroimaging data JO: Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=24128143 #FOAvip The aim of this paper is to develop a class of spatial transformation models (STM) to spatially model the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) and a set of covariates. Our STMs include a varying Box-Cox transformation model for dealing with the issue of non-Gaussian distributed imaging data and a Gaussian Markov Random Field model for incorporating spatial smoothness of the imaging data. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations and real data analysis demonstrate that the STM significantly outperforms the voxel-wise linear model with Gaussian noise in recovering meaningful geometric patterns. Our STM is able to reveal important brain regions with morphological changes in children with attention deficit hyperactivity disorder. Twit Text FOAVip Bayesian spatial transformation models with applications in neuroimaging data ????Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=24128143 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24128143 #FOAvip English Wikipedia <ref>{{Cite pmid|24128143}}</ref> Bayesian spatial transformation models with applications in neuroimaging data #MMPMID24128143 Miranda MF; Zhu H; Ibrahim JG Biometrics 2013[Dec]; 69 (4): ä PMID24128143show ga The aim of this paper is to develop a class of spatial transformation models (STM) to spatially model the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) and a set of covariates. Our STMs include a varying Box-Cox transformation model for dealing with the issue of non-Gaussian distributed imaging data and a Gaussian Markov Random Field model for incorporating spatial smoothness of the imaging data. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations and real data analysis demonstrate that the STM significantly outperforms the voxel-wise linear model with Gaussian noise in recovering meaningful geometric patterns. Our STM is able to reveal important brain regions with morphological changes in children with attention deficit hyperactivity disorder. äBayesian spatial transformation models with applications in neuroimagi(epmc).pdf DeepDyve Pubget Overpricing