Spatial Shrinkage Via the Product Independent Gaussian Process Prior

[1]  Brian J. Reich,et al.  Spatial Signal Detection Using Continuous Shrinkage Priors , 2019, Technometrics.

[2]  Xiao Wang,et al.  Local Region Sparse Learning for Image-on-Scalar Regression , 2016, ArXiv.

[3]  Russell T. Shinohara,et al.  Fully Bayesian spectral methods for imaging data , 2018, Biometrics.

[4]  Ciprian M Crainiceanu,et al.  Smooth Scalar-on-Image Regression via Spatial Bayesian Variable Selection , 2014, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

[5]  David Higdon,et al.  Non-Stationary Spatial Modeling , 2022, 2212.08043.

[6]  Oleg Okun,et al.  Bayesian Variable Selection , 2014 .

[7]  J. Griffin,et al.  Inference with normal-gamma prior distributions in regression problems , 2010 .

[8]  Snehashis Roy,et al.  Random forest regression for magnetic resonance image synthesis , 2017, Medical Image Anal..

[9]  John Best.  Magnetic resonance — the image! , 1988, The Medical journal of Australia.

[10]  Huixia Judy Wang,et al.  Variable selection in quantile varying coefficient models with longitudinal data , 2013, Comput. Stat. Data Anal..

[11]  Xiao Wang,et al.  Generalized Scalar-on-Image Regression Models via Total Variation , 2017, Journal of the American Statistical Association.

[12]  John Hughes,et al.  Fast, fully Bayesian spatiotemporal inference for fMRI data. , 2016, Biostatistics.

[13]  Ying Liu,et al.  Smooth Image-on-Scalar Regression for Brain Mapping , 2017 .

[14]  James G. Scott,et al.  The horseshoe estimator for sparse signals , 2010 .

[15]  R. Tibshirani,et al.  Sparsity and smoothness via the fused lasso , 2005 .

[16]  James A. Coan,et al.  Spatial Bayesian variable selection and grouping for high-dimensional scalar-on-image regression , 2015, 1509.04069.

[17]  Martin Kliesch,et al.  On the distribution of a product of N Gaussian random variables , 2017, Optical Engineering + Applications.

[18]  Snehashis Roy,et al.  Magnetic Resonance Image Example-Based Contrast Synthesis , 2013, IEEE Transactions on Medical Imaging.

[19]  Emad Fatemizadeh,et al.  Multiple Sclerosis Diagnosis Based on Analysis of Subbands of 2-D Wavelet Transform Applied on MR-images , 2007, 2007 IEEE/ACS International Conference on Computer Systems and Applications.

[20]  Robert E. Gaunt.  Products of normal, beta and gamma random variables: Stein operators and distributional theory , 2015, 1507.07696.

[21]  Ming Yang,et al.  Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine , 2016, Simul..

[22]  D. Nychka,et al.  A Multiresolution Gaussian Process Model for the Analysis of Large Spatial Datasets , 2015 .

[23]  N. Pillai,et al.  Dirichlet–Laplace Priors for Optimal Shrinkage , 2014, Journal of the American Statistical Association.

[24]  John-Dylan Haynes,et al.  Multi-scale classification of disease using structural MRI and wavelet transform , 2012, NeuroImage.

[25]  A. Wood,et al.  Simulation of Stationary Gaussian Processes in [0, 1] d , 1994 .

[26]  Tetyana Pavlenko,et al.  Measures of multivariate skewness and kurtosis in high-dimensional framework , 2014, SUT Journal of Mathematics.

[27]  Snehashis Roy,et al.  MR image synthesis by contrast learning on neighborhood ensembles , 2015, Medical Image Anal..

[28]  Russell T. Shinohara,et al.  Statistical estimation of T 1 relaxation times using conventional magnetic resonance imaging , 2016, NeuroImage.

[29]  C M Crainiceanu,et al.  Automatic Lesion Incidence Estimation and Detection in Multiple Sclerosis Using Multisequence Longitudinal MRI , 2013, American Journal of Neuroradiology.

[30]  J. Rosenthal,et al.  Optimal scaling of discrete approximations to Langevin diffusions , 1998 .

[31]  M. Fuentes,et al.  Circulant Embedding of Approximate Covariances for Inference From Gaussian Data on Large Lattices , 2017 .

[32]  Michael L. Stein,et al.  Bayesian and Maximum Likelihood Estimation for Gaussian Processes on an Incomplete Lattice , 2014, 1402.4281.

[33]  Russell T. Shinohara,et al.  Population-wide principal component-based quantification of blood–brain-barrier dynamics in multiple sclerosis , 2011, NeuroImage.

[34]  Jeffrey S. Morris,et al.  AUTOMATED ANALYSIS OF QUANTITATIVE IMAGE DATA USING ISOMORPHIC FUNCTIONAL MIXED MODELS, WITH APPLICATION TO PROTEOMICS DATA. , 2011, The annals of applied statistics.

[35]  Russell T. Shinohara,et al.  Relating multi-sequence longitudinal intensity profiles and clinical covariates in new multiple sclerosis lesions , 2015, 1509.08359.

[36]  K. Mardia.  Measures of multivariate skewness and kurtosis with applications , 1970 .

[37]  C. Almli,et al.  Unbiased nonlinear average age-appropriate brain templates from birth to adulthood , 2009, NeuroImage.

[38]  Hohsuk Noh,et al.  SPARSE VARYING COEFFICIENT MODELS FOR LONGITUDINAL DATA , 2010 .

[39]  Huimin Lu,et al.  Multiple Sclerosis Detection Based on Biorthogonal Wavelet Transform, RBF Kernel Principal Component Analysis, and Logistic Regression , 2016, IEEE Access.

[40]  Russell T. Shinohara,et al.  A LAG FUNCTIONAL LINEAR MODEL FOR PREDICTION OF MAGNETIZATION TRANSFER RATIO IN MULTIPLE SCLEROSIS LESIONS. , 2016, The annals of applied statistics.

[41]  C. Crainiceanu,et al.  Statistical normalization techniques for magnetic resonance imaging , 2014, NeuroImage: Clinical.

[42]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[43]  John A. Rice,et al.  Displaying the important features of large collections of similar curves , 1992 .

[44]  S. Duane,et al.  Hybrid Monte Carlo , 1987 .

[45]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[46]  Montserrat Fuentes,et al.  Spatial variable selection methods for investigating acute health effects of fine particulate matter components , 2015, Biometrics.

[47]  Ciprian M. Crainiceanu,et al.  refund: Regression with Functional Data , 2013 .

[48]  Jaeyong Lee,et al.  GENERALIZED DOUBLE PARETO SHRINKAGE. , 2011, Statistica Sinica.

[49]  Howard H. Chang,et al.  A spectral method for spatial downscaling , 2014, Biometrics.

[50]  Russell T. Shinohara,et al.  A Spatio-Temporal Model for Longitudinal Image-on-Image Regression , 2019, Statistics in biosciences.

[51]  T. J. Mitchell,et al.  Bayesian Variable Selection in Linear Regression , 1988 .

[52]  C. F. Sirmans,et al.  Spatial Modeling With Spatially Varying Coefficient Processes , 2003 .

[53]  S. Ghosal,et al.  Adaptive Bayesian Procedures Using Random Series Priors , 2014, 1403.0625.

[54]  B. Reich,et al.  Scalar‐on‐image regression via the soft‐thresholded Gaussian process , 2016, Biometrika.

[55]  Marina Vannucci,et al.  A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data , 2016 .