Intersectoral and spatial spill-overs of firms’ bankruptcy in Spain

This paper employs provincial data to study the spatial and intersectoral spill-overs in aggregate failure rates in Spain, by using an Integrated Nested Laplace Approximation. The analysis is based on NUTS3 data over the time span 2005Q1-2013Q4. By speculating on the effects of the Spanish financial crisis, we document empirical evidence of the presence of spatial spill-overs among neighboring counties. Furthermore, some intersectoral spill-overs are also detected: we observe that Industry and Agriculture exhibit a positive impact on the Service sector. These results can be useful to design proper policy rules to better manage the spread of bankruptcies over time and space.

A Comparison of Nonergodic Ground-Motion Models based on Geographically Weighted Regression and the Integrated Nested Laplace Approximation

Different nonergodic Ground-Motion Models based on spatially varying coefficient models are compared for ground-motion data in Italy. The models are based different methodologies: Multi-source geographically weighted regression (Caramenti et al., 2020), and Bayesian hierarchical models estimated with the integrated nested Laplace approximation (Rue et al., 2009). The different models are compared in terms of their predictive performance, their spatial coefficients, and their predictions. Models that include spatial terms perform slightly better than a simple base model that includes only event and station terms, in terms of out-of sample error based on cross-validation. The Bayesian spatial models have slightly lower generalization error, which can be attributed to the fact that they can include random effects for events and stations. The different methodologies give rise to different dependencies of the spatially varying terms on event and station locations, leading to between-model uncertainty in their predictions, which should be accommodated in a nonergodic seismic hazard assessment.

Using a Bayesian Modelling Approach to the Understand the Redistribution of Pottery to Romano-British Rural Sites

The vast majority of inhabitants of Roman Britain lived in the countryside. However, the redistribution of pottery to Romano-British rural sites is poorly understood. To address this issue, a Bayesian approach (Integrated Nested Laplace Approximation, INLA) is used to model the count of pottery recovered from rural sites. Through model selection, the redistribution of pottery was identified to be driven by small towns and pottery production sites. Findings also suggest that the redistribution of pottery was not homogenous across site types, with Roman roads and local trackways playing an important role.

Efficient and flexible Integration of variant characteristics in rare variant association studies using integrated nested Laplace approximation

Rare variants are thought to play an important role in the etiology of complex diseases and may explain a significant fraction of the missing heritability in genetic disease studies. Next-generation sequencing facilitates the association of rare variants in coding or regulatory regions with complex diseases in large cohorts at genome-wide scale. However, rare variant association studies (RVAS) still lack power when cohorts are small to medium-sized and if genetic variation explains a small fraction of phenotypic variance. Here we present a novel Bayesian rare variant Association Test using Integrated Nested Laplace Approximation (BATI). Unlike existing RVAS tests, BATI allows integration of individual or variant-specific features as covariates, while efficiently performing inference based on full model estimation. We demonstrate that BATI outperforms established RVAS methods on realistic, semi-synthetic whole-exome sequencing cohorts, especially when using meaningful biological context, such as functional annotation. We show that BATI achieves power above 70% in scenarios in which competing tests fail to identify risk genes, e.g. when risk variants in sum explain less than 0.5% of phenotypic variance. We have integrated BATI, together with five existing RVAS tests in the ‘Rare Variant Genome Wide Association Study’ (rvGWAS) framework for data analyzed by whole-exome or whole genome sequencing. rvGWAS supports rare variant association for genes or any other biological unit such as promoters, while allowing the analysis of essential functionalities like quality control or filtering. Applying rvGWAS to a Chronic Lymphocytic Leukemia study we identified eight candidate predisposition genes, including EHMT2 and COPS7A.

Bayesian Model Averaging with the Integrated Nested Laplace Approximation

The integrated nested Laplace approximation (INLA) for Bayesian inference is an efficient approach to estimate the posterior marginal distributions of the parameters and latent effects of Bayesian hierarchical models that can be expressed as latent Gaussian Markov random fields (GMRF). The representation as a GMRF allows the associated software R-INLA to estimate the posterior marginals in a fraction of the time as typical Markov chain Monte Carlo algorithms. INLA can be extended by means of Bayesian model averaging (BMA) to increase the number of models that it can fit to conditional latent GMRF. In this paper, we review the use of BMA with INLA and propose a new example on spatial econometrics models.

Spatial Models for Infants HIV/AIDS Incidence Using an Integrated Nested Laplace Approximation Approach

Background Kenya has made significant progress in the elimination of mother to child transmission of HIV through increasing access to HIV treatment and improving the health and well-being of women and children living with HIV. Despite this progress, broad geographical inequalities in infant HIV outcomes still exist. This study aimed at assessing the spatial distribution of HIV amongst infants, areas of abnormally high risk and associated risk factors for mother to child transmission of HIV using INLA and SPDE approach. Methods Data were obtained from the Early infant diagnosis (EID) database that is routinely collected for infants under one year for the year 2017. We performed both areal and point-reference analysis. Bayesian hierarchical Poisson models with spatially structured random effects were fitted to the data to examine the effects of the covariates on infant HIV risk. Spatial random effects were modelled using Conditional autoregressive model (CAR) and stochastic partial differential equations (SPDEs). Inference was done using Integrated Nested Laplace Approximation. Posterior probabilities for exceedance were produced to assess areas where the risk exceeds 1. The Deviance Information Criteria (DIC) selection was used for model comparison and selection. Results CAR model outperformed similar competing models in modeling and mapping HIV Relative Risk in Kenya. It had a smaller DIC among the rest (DIC = 306.36)) The SPDE model outperformed the spatial GLM model based on the DIC statistic. Highly active antiretroviral therapy (HAART) and breastfeeding were found to be negatively and positively associated with infant HIV positivity respectively [-0.125, 95% Credible Interval (Cred. Int.)= -0.348, -0.102], [0.178, 95% Cred. Int. -0.051, 0.412].Conclusion The study provides relevant strategic information required to make investment decisions for targeted high impact interventions to reduce HIV infections among infants in Kenya.