Compositionally-warped additive mixed modeling for a wide variety of non-Gaussian spatial data

Murakami, Daisuke; Kajita, Mami; Kajita, Seiji; Matsui, Tomoko

doi:10.1016/j.spasta.2021.100520

Cited by 5 publications

(1 citation statement)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rios & Tobar (2019) extend the warped GP to the "compositionally warped GP" by expressing g ϑ (•) as a (deep) composition of elementary functions which have explicit derivatives and inverses; these include the Box-Cox transform and the Sinh-Arcsinh transform. Murakami et al (2021) use the compositionally warped GP in a spatial mixed-model setting, while Maroñas et al (2021) propose a computationallyefficient variational algorithm for fitting the model. Note that, unlike in conventional generalized linear models (GLMs), no distribution is pre-specified for the {Z i }, which depends on ϑ that needs to be estimated.…”

Section: Deep Learning For Characterizing Complex Data Modelsmentioning

confidence: 99%

Statistical Deep Learning for Spatial and Spatio-Temporal Data

Wikle¹,

Zammit‐Mangion²

2022

Preprint

View full text Add to dashboard Cite

Deep neural network models have become ubiquitous in recent years, and have been applied to nearly all areas of science, engineering, and industry. These models are particularly useful for data that have strong dependencies in space (e.g., images) and time (e.g., sequences). Indeed, deep models have also been extensively used by the statistical community to model spatial and spatio-temporal data through, for example, the use of multi-level Bayesian hierarchical models and deep Gaussian processes. In this review, we first present an overview of traditional statistical and machine learning perspectives for modeling spatial and spatio-temporal data, and then focus on a variety of hybrid models that have recently been developed for latent process, data, and parameter specifications. These hybrid models integrate statistical modeling ideas with deep neural network models in order to take advantage of the strengths of each modeling paradigm. We conclude by giving an overview of computational technologies that have proven useful for these hybrid models, and with a brief discussion on future research directions.

show abstract

Section: Deep Learning For Characterizing Complex Data Modelsmentioning

confidence: 99%

Statistical Deep Learning for Spatial and Spatio-Temporal Data

Wikle¹,

Zammit‐Mangion²

2022

Preprint

View full text Add to dashboard Cite

show abstract

Spherical Poisson point process intensity function modeling and estimation with measure transport

Ng¹,

Zammit‐Mangion²

2022

Spatial Statistics

View full text Add to dashboard Cite

Statistical Deep Learning for Spatial and Spatiotemporal Data

Wikle

Zammit‐Mangion

2023

Annu. Rev. Stat. Appl.

View full text Add to dashboard Cite

Deep neural network models have become ubiquitous in recent years and have been applied to nearly all areas of science, engineering, and industry. These models are particularly useful for data that have strong dependencies in space (e.g., images) and time (e.g., sequences). Indeed, deep models have also been extensively used by the statistical community to model spatial and spatiotemporal data through, for example, the use of multilevel Bayesian hierarchical models and deep Gaussian processes. In this review, we first present an overview of traditional statistical and machine learning perspectives for modeling spatial and spatiotemporal data, and then focus on a variety of hybrid models that have recently been developed for latent process, data, and parameter specifications. These hybrid models integrate statistical modeling ideas with deep neural network models in order to take advantage of the strengths of each modeling paradigm. We conclude by giving an overview of computational technologies that have proven useful for these hybrid models, and with a brief discussion on future research directions.

show abstract

Compositionally-warped additive mixed modeling for a wide variety of non-Gaussian spatial data

Cited by 5 publications

References 44 publications

Statistical Deep Learning for Spatial and Spatio-Temporal Data

Statistical Deep Learning for Spatial and Spatio-Temporal Data

Spherical Poisson point process intensity function modeling and estimation with measure transport

Statistical Deep Learning for Spatial and Spatiotemporal Data

Contact Info

Product

Resources

About