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

01/11/2021
by   Daisuke Murakami, et al.
0

As with the advancement of geographical information systems, non-Gaussian spatial data is getting larger and more diverse. Considering this background, this study develops a general framework for fast and flexible non-Gaussian regression, especially for spatial/spatiotemporal modeling. The developed model, termed the compositionally-warped additive mixed model (CAMM), combines an additive mixed model (AMM) and the compositionally-warped Gaussian process to model a wide variety of non-Gaussian continuous data including spatial and other effects. Specific advantages of the proposed CAMM requires no explicit assumption of data distribution unlike existing AMMs, and fast estimation through a restricted likelihood maximization balancing the modeling accuracy and complexity. Monte Carlo experiments show the estimation accuracy and computational efficiency of CAMM for modeling non-Gaussian data including fat-tailed and/or skewed distributions. Finally, the proposed approach is applied to crime data to examine the empirical performance of the regression analysis and prediction. The proposed approach is implemented in an R package spmoran. See details on how to implement CAMM, see https://github.com/dmuraka/spmoran.

READ FULL TEXT
research
07/26/2019

A memory-free spatial additive mixed modeling for big spatial data

This study develops a spatial additive mixed modeling (AMM) approach est...
research
09/14/2021

Transformation-based generalized spatial regression using the spmoran package: Case study examples

This study presents application examples of generalized spatial regressi...
research
05/20/2020

Balancing spatial and non-spatial variation in varying coefficient modeling: a remedy for spurious correlation

This study discusses the importance of balancing spatial and non-spatial...
research
10/06/2018

Low rank spatial econometric models

This article presents a re-structuring of spatial econometric models in ...
research
04/28/2021

Improved log-Gaussian approximation for over-dispersed Poisson regression: application to spatial analysis of COVID-19

In the era of open data, Poisson and other count regression models are i...
research
03/12/2021

Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models

We define a novel class of additive models called Extended Latent Gaussi...
research
06/08/2022

TreeFlow: Going beyond Tree-based Gaussian Probabilistic Regression

The tree-based ensembles are known for their outstanding performance for...

Please sign up or login with your details

Forgot password? Click here to reset