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A projection-based approach for spatial generalized linear mixed models Haran, Murali
Description
Non-Gaussian spatial data arise in a number of disciplines. Examples include spatial data on disease incidences (counts), and satellite images of ice sheets (presence-absence). Spatial generalized linear mixed models (SGLMMs), which build on latent Gaussian processes or Gaussian Markov random fields, are convenient and flexible models for such data and are used widely in mainstream statistics and other disciplines. For high-dimensional data, SGLMMs present significant computational challenges due to the large number of dependent spatial random effects. Furthermore, spatial confounding makes the regression coefficients challenging to interpret. I will discuss projection-based approaches that reparameterize and reduce the number of random effects in SGLMMs, thereby improving the efficiency of Markov chain Monte Carlo (MCMC) algorithms for inference. Our approach also addresses spatial confounding issues. This talk is based on joint work with Yawen Guan (SAMSI) and John Hughes (U of Colorado-Denver).
Item Metadata
Title |
A projection-based approach for spatial generalized linear mixed models
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Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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Date Issued |
2017-12-05T10:47
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Description |
Non-Gaussian spatial data arise in a number of disciplines. Examples include spatial data on disease
incidences (counts), and satellite images of ice sheets (presence-absence). Spatial generalized linear mixed models
(SGLMMs), which build on latent Gaussian processes or Gaussian Markov random fields, are convenient and flexible
models for such data and are used widely in mainstream statistics and other disciplines. For high-dimensional data,
SGLMMs present significant computational challenges due to the large number of dependent spatial random effects.
Furthermore, spatial confounding makes the regression coefficients challenging to interpret. I will discuss
projection-based approaches that reparameterize and reduce the number of random effects in SGLMMs, thereby
improving the efficiency of Markov chain Monte Carlo (MCMC) algorithms for inference. Our approach also addresses
spatial confounding issues. This talk is based on joint work with Yawen Guan (SAMSI) and John Hughes (U of
Colorado-Denver).
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Extent |
55 minutes
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Subject | |
Type | |
File Format |
video/mp4
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Language |
eng
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Notes |
Author affiliation: Pennsylvania State University
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Series | |
Date Available |
2018-06-28
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0368758
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URI | |
Affiliation | |
Peer Review Status |
Unreviewed
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Scholarly Level |
Faculty
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International