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The node-wise pseudo-marginal method : model selection with spatial dependence on latent graphs
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Thesingarajah, Denishrouf and Johansen, Adam M. (2022) The node-wise pseudo-marginal method : model selection with spatial dependence on latent graphs. Statistics and Computing, 32 . 43. doi:10.1007/s11222-022-10101-6 ISSN 0960-3174.
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WRAP-node-wise-pseudo-marginal-model-spatial-dependence-latent-graphs-2022.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (2589Kb) |
Official URL: https://doi.org/10.1007/s11222-022-10101-6
Abstract
Motivated by problems from neuroimaging in which existing approaches make use of “mass univariate” analysis which neglects spatial structure entirely, but the full joint modelling of all quantities of interest is computationally infeasible, a novel method for incorporating spatial dependence within a (potentially large) family of model-selection problems is presented. Spatial dependence is encoded via a Markov random field model for which a variant of the pseudo-marginal Markov chain Monte Carlo algorithm is developed and extended by a further augmentation of the underlying state space. This approach allows the exploitation of existing unbiased marginal likelihood estimators used in settings in which spatial independence is normally assumed thereby facilitating the incorporation of spatial dependence using non-spatial estimates with minimal additional development effort. The proposed algorithm can be realistically used for analysis of moderately sized data sets such as 2D slices of whole 3D dynamic PET brain images or other regions of interest. Principled approximations of the proposed method, together with simple extensions based on the augmented spaces, are investigated and shown to provide similar results to the full pseudo-marginal method. Such approximations and extensions allow the improved performance obtained by incorporating spatial dependence to be obtained at negligible additional cost. An application to measured PET image data shows notable improvements in revealing underlying spatial structure when compared to current methods that assume spatial independence.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > QA Mathematics | |||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Spatial analysis (Statistics), Tomography, Emission -- Data processing, Image processing -- Digital techniques, Bayesian statistical decision theory -- Data processing, Simulated annealing (Mathematics) | |||||||||||||||
Journal or Publication Title: | Statistics and Computing | |||||||||||||||
Publisher: | Springer | |||||||||||||||
ISSN: | 0960-3174 | |||||||||||||||
Official Date: | 25 May 2022 | |||||||||||||||
Dates: |
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Volume: | 32 | |||||||||||||||
Article Number: | 43 | |||||||||||||||
DOI: | 10.1007/s11222-022-10101-6 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
Date of first compliant deposit: | 3 May 2022 | |||||||||||||||
Date of first compliant Open Access: | 25 May 2022 | |||||||||||||||
RIOXX Funder/Project Grant: |
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