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spatial generalized additive

Intercept-only spatial generalized additive models (GAM) were used to evaluate and visualize spatial variation within the RdP community.

A Local View of Informal Urban Environments: a Mobile Phone-Based Neighborhood Audit of Street-Level Factors in a Brazilian Informal Community

spatial generalized additive

Spatial variation in odds of acute dengue was modeled using a spatial generalized additive model predicted onto a grid of coordinate pairs covering the Southern Province.

Geospatial analysis of dengue emergence in rural areas in the Southern Province of Sri Lanka.

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spatial generalized linear

ABSTRACT Non-Gaussian spatial responses are usually modeled using spatial generalized linear mixed model with spatial random effects.

Approximate composite marginal likelihood inference in spatial generalized linear mixed models

spatial generalized linear

In this article, small area models are proposed for the class of spatial generalized linear mixed models to obtain small area predictors and corresponding second‐order unbiased mean squared prediction errors via Taylor expansion and a parametric bootstrap approach.

Spatial generalized linear mixed models in small area estimation

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10.1007/s11524-019-00351-7

Intercept-only spatial generalized additive models (GAM) were used to evaluate and visualize spatial variation within the RdP community.

A Local View of Informal Urban Environments: a Mobile Phone-Based Neighborhood Audit of Street-Level Factors in a Brazilian Informal Community

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10.1093/trstmh/trz123

Spatial variation in odds of acute dengue was modeled using a spatial generalized additive model predicted onto a grid of coordinate pairs covering the Southern Province.

Geospatial analysis of dengue emergence in rural areas in the Southern Province of Sri Lanka.

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10.1109/ICCV.2019.00074

In this paper, we propose to learn pairwise relations among data points in a global fashion to improve semantic segmentation with arbitrarily-structured data, through spatial generalized propagation networks (SGPN).

Learning Propagation for Arbitrarily-Structured Data

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10.1080/02664763.2018.1506020

ABSTRACT Non-Gaussian spatial responses are usually modeled using spatial generalized linear mixed model with spatial random effects.

Approximate composite marginal likelihood inference in spatial generalized linear mixed models

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10.1093/noajnl/vdz014.138

Topographical analyses were conducted using logistic and multinomial spatial generalized additive models (GAM).

SURG-03. SPATIAL COORDINATES FROM GAMMA KNIFE RADIOSURGERY REVEAL PRIMARY CANCERS HAVE REGIONAL CNS TOPOGRAPHICAL DISTRIBUTION FOR BRAIN METASTASES

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10.1002/cjs.11502

In this article, small area models are proposed for the class of spatial generalized linear mixed models to obtain small area predictors and corresponding second‐order unbiased mean squared prediction errors via Taylor expansion and a parametric bootstrap approach.

Spatial generalized linear mixed models in small area estimation

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10.11591/IJECE.V9I3.PP1732-1741

First, the spatial generalized mean Ambiguity function of the observations across the array is constructed.

Blind separation of complex-valued satellite-AIS data for marine surveillance: a spatial quadratic time-frequency domain approach

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10.1016/j.aap.2019.02.029

This study develops a Bayesian spatial generalized ordered logit model with conditional autoregressive priors to examine severity of freeway crashes.

Analyzing freeway crash severity using a Bayesian spatial generalized ordered logit model with conditional autoregressive priors.

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10.1101/586909

In this paper, we develop a dynamic occupancy model that uses a spatial generalized additive model to estimate non-linear spatial variation in occupancy not accounted for by environmental covariates.

Estimating spatially and temporally complex range dynamics when detection is imperfect

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10.1038/s41598-019-48851-5

In this paper, we develop a dynamic occupancy model that uses a spatial generalized additive model to estimate non-linear spatial variation in occupancy not accounted for by environmental covariates.

Modeling spatially and temporally complex range dynamics when detection is imperfect

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10.1016/j.envint.2018.12.012

We used spatial Generalized Linear Mixed Models to evaluate the associations between air pollutants and CVD prevalence, adjusting for multiple covariates.

Ambient PM1 air pollution and cardiovascular disease prevalence: Insights from the 33 Communities Chinese Health Study.

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10.1002/ENV.2508

Our results show that in general, max‐stable models yield return level estimates with similar mean squared error, and the spatial generalized extreme value model also provides comparable estimates.

Assessing models for estimation and methods for uncertainty quantification for spatial return levels

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Spatial Generalized
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What is/are Spatial Generalized?

Spatial Generalized - Intercept-only spatial generalized additive models (GAM) were used to evaluate and visualize spatial variation within the RdP community. [1] Spatial variation in odds of acute dengue was modeled using a spatial generalized additive model predicted onto a grid of coordinate pairs covering the Southern Province. [2] In this paper, we propose to learn pairwise relations among data points in a global fashion to improve semantic segmentation with arbitrarily-structured data, through spatial generalized propagation networks (SGPN). [3] ABSTRACT Non-Gaussian spatial responses are usually modeled using spatial generalized linear mixed model with spatial random effects. [4] Topographical analyses were conducted using logistic and multinomial spatial generalized additive models (GAM). [5] In this article, small area models are proposed for the class of spatial generalized linear mixed models to obtain small area predictors and corresponding second‐order unbiased mean squared prediction errors via Taylor expansion and a parametric bootstrap approach. [6] First, the spatial generalized mean Ambiguity function of the observations across the array is constructed. [7] This study develops a Bayesian spatial generalized ordered logit model with conditional autoregressive priors to examine severity of freeway crashes. [8] In this paper, we develop a dynamic occupancy model that uses a spatial generalized additive model to estimate non-linear spatial variation in occupancy not accounted for by environmental covariates. [9] In this paper, we develop a dynamic occupancy model that uses a spatial generalized additive model to estimate non-linear spatial variation in occupancy not accounted for by environmental covariates. [10] We used spatial Generalized Linear Mixed Models to evaluate the associations between air pollutants and CVD prevalence, adjusting for multiple covariates. [11] Our results show that in general, max‐stable models yield return level estimates with similar mean squared error, and the spatial generalized extreme value model also provides comparable estimates. [12]

spatial generalized additive

Intercept-only spatial generalized additive models (GAM) were used to evaluate and visualize spatial variation within the RdP community. [1] Spatial variation in odds of acute dengue was modeled using a spatial generalized additive model predicted onto a grid of coordinate pairs covering the Southern Province. [2] Topographical analyses were conducted using logistic and multinomial spatial generalized additive models (GAM). [3] In this paper, we develop a dynamic occupancy model that uses a spatial generalized additive model to estimate non-linear spatial variation in occupancy not accounted for by environmental covariates. [4] In this paper, we develop a dynamic occupancy model that uses a spatial generalized additive model to estimate non-linear spatial variation in occupancy not accounted for by environmental covariates. [5]

spatial generalized linear

ABSTRACT Non-Gaussian spatial responses are usually modeled using spatial generalized linear mixed model with spatial random effects. [1] In this article, small area models are proposed for the class of spatial generalized linear mixed models to obtain small area predictors and corresponding second‐order unbiased mean squared prediction errors via Taylor expansion and a parametric bootstrap approach. [2] We used spatial Generalized Linear Mixed Models to evaluate the associations between air pollutants and CVD prevalence, adjusting for multiple covariates. [3]