About predictions in spatial autoregressive models: optimal and almost optimal strategies

Goulard, Michel; Laurent, Thibault; Thomas‐Agnan, Christine

doi:10.1080/17421772.2017.1300679

Cited by 61 publications

(25 citation statements)

References 22 publications

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“…To identify areas where TB under-detection and diagnosis are expected to occur, the best fitting model was used to predict the expected CNR based on the covariates (e.g., trend) and spatial dependencies (e.g., signal) in the data [28]. Next, a standardized CNR, accounting for effects of poverty, testing and retreatment rate, was calculated by subtracting the predicted trend component from the observed CNR.…”

Section: Discussionmentioning

confidence: 99%

A Spatial Analysis Framework to Monitor and Accelerate Progress towards SDG 3 to End TB in Bangladesh

Rood

Khan

Modak

et al. 2018

IJGI

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Global efforts to end the tuberculosis (TB) epidemic by 2030 (SDG3.3) through improved TB case detection and treatment have not been effective to significantly reduce the global burden of the TB epidemic. This study presents an analytical framework to evaluate the use of TB case notification rates (CNR) to monitor and to evaluate TB under-detection and under-diagnoses in Bangladesh. Local indicators of spatial autocorrelation (LISA) were calculated to assess the presence and scale of spatial clusters of TB CNR across 489 upazilas in Bangladesh. Simultaneous autoregressive models were fit to the data to identify associations between TB CNR and poverty, TB testing rates and retreatment rates. CNRs were found to be significantly spatially clustered, negatively correlated to poverty rates and positively associated to TB testing and retreatment rates. Comparing the observed pattern of CNR with model-standardized rates made it possible to identify areas where TB under-detection is likely to occur. These results suggest that TB CNR is an unreliable proxy for TB incidence. Spatial variations in TB case notifications and subnational variations in TB case detection should be considered when monitoring national TB trends. These results provide useful information to target and prioritize context specific interventions.

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Section: Discussionmentioning

confidence: 99%

A Spatial Analysis Framework to Monitor and Accelerate Progress towards SDG 3 to End TB in Bangladesh

Rood

Khan

Modak

et al. 2018

IJGI

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“…2.3, for a more detailed introduction to SAR models. A general discussion about predictions of SAR models from a frequentist perspective can be found in Goulard et al (2017). If we have ∼ N(0, σ 2 I ), with residual variance σ 2 and identity matrix I of dimension N , it follows that…”

Section: Example: Lagged Sar Modelsmentioning

confidence: 99%

Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models

2020

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Cross-validation can be used to measure a model’s predictive accuracy for the purpose of model comparison, averaging, or selection. Standard leave-one-out cross-validation (LOO-CV) requires that the observation model can be factorized into simple terms, but a lot of important models in temporal and spatial statistics do not have this property or are inefficient or unstable when forced into a factorized form. We derive how to efficiently compute and validate both exact and approximate LOO-CV for any Bayesian non-factorized model with a multivariate normal or Student-$$t$$ t distribution on the outcome values. We demonstrate the method using lagged simultaneously autoregressive (SAR) models as a case study.

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“…9 In turn, the case where o = d = 0 yields a SEM or "Spatial Error model", where the disturbances follow a spatial autoregressive process. 10 Finally, a model where all o , d , o and d parameters are non zero implies a SAC or "Spatial Autocorrelation model", which allows for spatial autoregressive dependence both in air transport ‡ows and the disturbances. 11 By taking di¤erent assumptions on the strength of dependence parameters o , d , o and d and for simplicity's sake, we study seven special models of (4), as follows.…”

Section: Spatial Dependencementioning

confidence: 99%

Spatial dependence in (origin‐destination) air passenger flows

Margaretic

Thomas‐Agnan

Doucet

2017

Papers in Regional Science

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International audienceWe explore the estimation of origin-destination (OD), city-pair, air passenger flows. Our dataset contains 279 cities, worldwide, over 2010-2012. Allowing for two gravity model specifications (log-normal and Poisson), we compare non-spatial and spatial models. We are the first to apply spatial econometric flow models and eigenfunction spatial filtering approaches to air transport. Distinguishing between origin, destination and network effects, we determine the impact and significance of a change in a variable in a given city, on flows originating from and going to that city and originating from a different city and going to an alternative city. Finally, we compare models based on different specifications of the spatial weight matrix

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About predictions in spatial autoregressive models: optimal and almost optimal strategies

Cited by 61 publications

References 22 publications

A Spatial Analysis Framework to Monitor and Accelerate Progress towards SDG 3 to End TB in Bangladesh

A Spatial Analysis Framework to Monitor and Accelerate Progress towards SDG 3 to End TB in Bangladesh

Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models

Spatial dependence in (origin‐destination) air passenger flows

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