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Item type:Publication, Bayesian spatial quantile modeling applied to the incidence of extreme poverty in Lima-Peru(Springer Science+Business Media, 2022-06-04)Peru is an emerging nation with a nonuniform development where the growth is focused on some specific cities and districts, as a result there is serious economic inequalities across the country. Despite the poverty in Peru has declined in the last decades, there is still poor districts in risk to become extremely poor, even in its capital, Lima. In this context, it is relevant to study the incidence of extreme poverty at district levels. In this paper, we propose to estimate the quantiles of the incidence of extreme poverty of districts in Lima by using spatial quantile models based on the Kumaraswamy distribution and spatial random effects for areal data. Furthermore, in order to deal with spatial confounding random effects we used the Spatial Orthogonal Centroid “K”orrection approach. Bayesian inference for these hierarchical models is conveniently performed based on the Hamiltonian Monte Carlo method. Our modeling is flexible and able to describe the quantiles of incidence of extreme poverty in Lima. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fast generalized spatial multilevel blockNNGP modeling(Elsevier B.V., 2024-01-01)Typical geostatistical models only consider the case where we observe one response at each location. However, the situation with multiple replicates at spatial locations is seldom discussed. Moreover, the generalized spatial Gaussian process models encounter computational difficulties when the size of the spatial domain becomes massive. Thus, fast generalized spatial multilevel models that use block nearest neighbor Gaussian process to scale to large datasets are introduced. The proposed method uses integrated nested Laplace approximation (INLA) to avoid long sequential updates of the Markov chain Monte Carlo (MCMC) methods. A simulation study is performed under different response distributions to show the model parameter estimation capacity, computational efficiency, and prediction performance. Finally, the proposed models are fitted to the data of Beijing housing transactions to predict the sales price of houses at unobserved locations. The studies demonstrate that the proposed models have advantages in fitting and prediction, making the interpretation better substantiated. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fast Bayesian Inference of Block Nearest Neighbor Gaussian Models for Large Data(Springer, 2023-04-01)This paper presents the development of a spatial block-Nearest Neighbor Gaussian process (blockNNGP) for location-referenced large spatial data. The key idea behind this approach is to divide the spatial domain into several blocks which are dependent under some constraints. The cross-blocks capture the large-scale spatial dependence, while each block captures the small-scale spatial dependence. The resulting blockNNGP enjoys Markov properties reflected on its sparse precision matrix. It is embedded as a prior within the class of latent Gaussian models, thus fast Bayesian inference is obtained using the integrated nested Laplace approximation. The performance of the blockNNGP is illustrated on simulated examples, a comparison of our approach with other methods for analyzing large spatial data and applications with Gaussian and non-Gaussian real data.