Estimating Spatial Econometrics Models with Integrated Nested Laplace Approximation

Integrated Nested Laplace Approximation provides a fast and effective method for marginal inference on Bayesian hierarchical models. This methodology has been implemented in the R-INLA package which permits INLA to be used from within R statistical software. Although INLA is implemented as a general methodology, its use in practice is limited to the models implemented in the R-INLA package. Spatial autoregressive models are widely used in spatial econometrics but have until now been missing from the R-INLA package. In this paper, we describe the implementation and application of a new class of latent models in INLA made available through R-INLA. This new latent class implements a standard spatial lag model, which is widely used and that can be used to build more complex models in spatial econometrics. The implementation of this latent model in R-INLA also means that all the other features of INLA can be used for model fitting, model selection and inference in spatial econometrics, as will be shown in this paper. Finally, we will illustrate the use of this new latent model and its applications with two datasets based on Gaussian and binary outcomes

Bayesian Multivariate Spatial Models for Lattice Data with INLA

The INLAMSM package for the R programming language provides a collection of multivariate spatial models for lattice data that can be used with package INLA for Bayesian inference. The multivariate spatial models include different structures to model the spatial variation of the variables and the between-variables variability. In this way, fitting multivariate spatial models becomes faster and easier. The use of the different models included in the package is illustrated using two different datasets: the well-known North Carolina SIDS data and mortality by three causes of death in Comunidad Valenciana (Spain)

A comparison of different methods for small area estimation

Government agencies often provide small area estimates that rely on available data and some underlying model that helps to provide estimates in all areas, even in those that were not sampled. Several models have been well-established for the study of data coming from small areas. In this paper we have made a comparison of some of these methods paying attention to how dierent types of data sets can be employed eciently and how to deal with the estimation in areas for which no di- rect individual data area available. We have considered design-based, regression and EBLUP estimators, which have been tted using both a likelihood-based and a Bayesian approach. Spatial correlation among areas has also been considered. As in any study of the performance of dierent models, we have also compared dierent criteria for model comparisson and selection

Bayesian Statistics Small Area Estimation

National statistical offices are often required to provide statistical information at several administrative or small area levels. Having good area level estimates is important because policies will often be based on this type of information. In this paper we describe how Bayesian hierarchical models can help in the task of providing good quality small area estimates. Starting from direct estimates obtained from survey data, we describe a range of Bayesian hierarchical models that incorporate different types of random effects and show that these give improved estimates. Models that synthesise individual and aggregated information are considered as well. Finally, we highlight some additional applications that further exploit the estimates produced, such as the classification of areas and how to approach the problem of missing data

Modeling spatial effects of PM₂.₅ on term low birth weight in Los Angeles County

Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM₂.₅) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure–response of PM₂.₅ on TLBW to be the same throughout a large geographical area. Health effects related to PM₂.₅ exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure–response relationship between individual-level exposure to PM₂.₅ and TLBW. Here, we examine the overall and spatially varying exposure–response relationship between PM₂.₅ and TLBW throughout urban Los Angeles (LA) County, California. We estimated PM₂.₅ from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM₂.₅ level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure–response for PM₂.₅ and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure–response estimates for PM₂.₅ on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effectiveKEYWORDS: Multilevel modeling, Term low birth weight, Air pollution, PM2.5, Spatial effects, PM₂.₅This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Elsevier. The published article can be found at: http://www.journals.elsevier.com/environmental-research

The mossy north : an inverse latitudinal diversity gradient in European bryophytes

It remains hotly debated whether latitudinal diversity gradients are common across taxonomic groups and whether a single mechanism can explain such gradients. Investigating species richness (SR) patterns of European land plants, we determine whether SR increases with decreasing latitude, as predicted by theory, and whether the assembly mechanisms differ among taxonomic groups. SR increases towards the south in spermatophytes, but towards the north in ferns and bryophytes. SR patterns in spermatophytes are consistent with their patterns of beta diversity, with high levels of nestedness and turnover in the north and in the south, respectively, indicating species exclusion towards the north and increased opportunities for speciation in the south. Liverworts exhibit the highest levels of nestedness, suggesting that they represent the most sensitive group to the impact of past climate change. Nevertheless, although the extent of liverwort species turnover in the south is substantially and significantly lower than in spermatophytes, liverworts share with the latter a higher nestedness in the north and a higher turn-over in the south, in contrast to mosses and ferns. The extent to which the similarity in the patterns displayed by spermatophytes and liverworts reflects a similar assembly mechanism remains, however, to be demonstrated.Peer reviewe

Global wealth disparities drive adherence to COVID-safe pathways in head and neck cancer surgery

Peer reviewe