A Bayesian Approach to Parameter Estimation in the Presence of Spatial Missing Data

Panzera, Domenica; Benedetti, Roberto; Postiglione, Paolo

doi:10.1080/17421772.2016.1102962

Cited by 11 publications

(6 citation statements)

References 41 publications

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“…The pycnophylactic property, in this case, is translated as the sum of estimates of the variable ln s k i for NUTS 3 units produces the variable at NUTS 2 level, where the sum is extended to the NUTS 3 units that belong to a particular NUTS 2 region. See [36] for further details.…”

Section: The Bayesian Interpolation Methodsmentioning

confidence: 99%

“…This choice could be seen as a way to treat specific sources of uncertainty in the case of spatial data. However, generally, other sources of uncertainty could require ad hoc methodologies for spatial models [32], and a range of procedures to deal with the accuracy of data, measurement error, and missing information have been proposed by [33][34][35][36].…”

Section: The Spatial Augmented Model Of Conditional β-Convergencementioning

confidence: 99%

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Economic Convergence in EU NUTS 3 Regions: A Spatial Econometric Perspective

2020

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The topic of economic convergence is still crucial in the European Union (EU) as promoting regional growth and the reduction of disparities remains a key objective. In this paper we investigate development and economic growth across EU regions. Particular attention is given to σ-convergence and β-convergence. These analyses are carried out for regional units corresponding to the third level of the NUTS (Nomenclature of Territorial Units for Statistics) classification. Focusing on a refined geographical scale could offer a detailed picture of the regional growth dynamics within the EU. Additionally, we use a spatial augmented version of the conditional β-convergence model to take into account the spatial interdependencies among regions. Findings shed light on the impact of spatial interaction effects and on the need of territorial policies to achieve convergence in the EU. This aspect highlights how coordinating the regional development policies between regions is pivotal to achieve economic, as well as social and political stability within the EU.

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Section: The Bayesian Interpolation Methodsmentioning

confidence: 99%

Section: The Spatial Augmented Model Of Conditional β-Convergencementioning

confidence: 99%

Economic Convergence in EU NUTS 3 Regions: A Spatial Econometric Perspective

2020

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“…Benedetti and Palma (1994) derive the parameters of this posterior. Other examples of this approach, similar to those used to deal with a disaggregation problem, can be seen in Panzera, Benedetti, and Postiglione (2016), where missing data in spatial models are addressed through BIM.…”

Section: Spatial Disaggregation Of Information Using a Microdata Apprmentioning

confidence: 99%

Mapping poverty at the local level in Europe: A consistent spatial disaggregation of the AROPE indicator for France, Spain, Portugal and the United Kingdom

Dapena

Vázquez

Morollón

et al. 2021

Regional Science Policy & Practice

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In the EU, territorial inequalities in terms of income and poverty have been broadly analysed at the national and regional levels. However, mainly due to the lack of reliable data, very little attention has been paid to territorial inequalities within European regions, namely, at a more local level, such as in metropolitan areas, cities or neighbourhoods. This paper proposes a methodology to disaggregate official regional poverty figures into poverty indicators for smaller spatial units, mainly local administrative units. For each country, poverty figures at the regional level from household surveys are combined with microcensus data that contain details on the local entities of residence to disaggregate the regional poverty indicator. In contrast to previous methodologies, our proposed technique guarantees consistency between the local poverty estimates and the regional poverty figures through a second step that adjusts the initial estimates based on generalized cross entropy. The procedure is applied for four European countries: France, Spain, the United Kingdom and Portugal. The resulting local estimates provide an intraregional map of poverty and some insights into the particular behaviour of the capital regions and the disparities between city centres and their surrounding areas.

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“…Several approaches have been suggested to accommodate missing covariate values, for example, multiple imputation, maximum likelihood, fully Bayesian, and weighted estimating equations (see Ibrahim et al 2005, for an overview of these methods). These same methods have been applied to the case where the covariates may be spatially dependent (e.g., Baker, White, and Mengersen 2014;Panzera, Bendetti, and Postiglione 2016;Rathbun 2013;Waagepetersen 2008;Zidek, Sun, and Le 2000). The fully Bayesian approach is to place a prior distribution on the missing covariate values and estimate their posterior distributions within the Bayesian framework.…”

Section: Research Goals and Contributionsmentioning

confidence: 99%

Modeling Bronchiolitis Incidence Proportions in the Presence of Spatio-Temporal Uncertainty

Heaton

Berrett

Pugh

et al. 2019

Journal of the American Statistical Association

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Bronchiolitis (inflammation of the lower respiratory tract) in infants is primarily due to viral infection and is the single most common cause of infant hospitalization in the United States. To increase epidemiological understanding of bronchiolitis (and, subsequently, develop better prevention strategies), this research analyzes data on infant bronchiolitis cases from the U.S. Military Health System between the years 2003-2013 in Norfolk, Virginia, USA. For privacy reasons, child home addresses, birth dates, and diagnosis dates were randomized (jittered) creating spatio-temporal uncertainty in the geographic location and timing of bronchiolitis incidents. Using spatio-temporal point patterns, we created a modeling strategy that accounts for the jittering to estimate and quantify the uncertainty for the incidence proportion (IP) of bronchiolitis. Additionally, we regress the IP onto key covariates including pollution where we adequately account for uncertainty in the pollution levels (i.e., covariate uncertainty) using a land use regression model. Our analysis results indicate that the IP is positively associated with sulfur dioxide and population density. Further, we demonstrate how scientific conclusions may change if various sources of uncertainty (either spatio-temporal or covariate uncertainty) are not accounted for. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.

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A Bayesian Approach to Parameter Estimation in the Presence of Spatial Missing Data

Cited by 11 publications

References 41 publications

Economic Convergence in EU NUTS 3 Regions: A Spatial Econometric Perspective

Economic Convergence in EU NUTS 3 Regions: A Spatial Econometric Perspective

Mapping poverty at the local level in Europe: A consistent spatial disaggregation of the AROPE indicator for France, Spain, Portugal and the United Kingdom

Modeling Bronchiolitis Incidence Proportions in the Presence of Spatio-Temporal Uncertainty

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