Environmetrics最新文献

Environmental monitoring is of particular importance for studying biodiversity and ecosystems. Many environmental monitoring programs emphasize plant registrations as part of their inventory responsibilities. Among the various methods available for surveying plant communities, we focus on presence/absence (P/A) sampling due to its underutilized potential. P/A sampling offers several advantages over other methods, particularly its efficiency in terms of time and cost. However, interpreting direct information from this type of data can be challenging, as the results are heavily dependent on plot size and species distribution patterns. To overcome these difficulties, model-based assumptions are necessary. In this article, we propose a method for estimating parameters of an inhomogeneous Neyman-Scott point process, specifically a Matérn cluster process, using P/A data. The inhomogeneity is modeled by allowing the offspring process intensity to vary with environmental covariates. The proposed estimators and their corresponding confidence intervals are evaluated through Monte Carlo simulations and empirical data (P/A registrations for three plant species) collected by surveyors in Northern Sweden. The results indicate that the method generally produces nearly unbiased estimators, particularly when the sample size is sufficiently large. These parameter estimates from the underlying inhomogeneous Neyman-Scott point process can subsequently be used to compute local estimates of expected plant density.

Spatial misalignment arises when datasets are aggregated or collected at different spatial scales, leading to information loss. We develop a Bayesian disaggregation framework that links misaligned data to a continuous-domain model through an iteratively linearised integration scheme implemented with the Integrated Nested Laplace Approximation (INLA). The framework accommodates different ways of handling observations depending on the application, resulting in four variants: (i) Raster at Full Resolution, (ii) Raster Aggregation, (iii) Polygon Aggregation (PolyAgg), and (iv) Point Values (PointVal). The first three represent increasing levels of spatial averaging, while the last two address situations with incomplete covariate information. For PolyAgg and PointVal, we reconstruct the covariate field using three strategies—Value Plugin, Joint Uncertainty, and Uncertainty Plugin—with the latter two propagating uncertainty. We illustrate the framework with an example motivated by landslide modelling, focusing on methodology rather than interpreting landslide processes. Simulations show that uncertainty-propagating approaches outperform Value Plugin method and remain robust under model misspecification. Point-pattern observations and full-resolution covariates are therefore preferable, and when covariate fields are incomplete, uncertainty-aware methods are most reliable. The framework is well suited to landslide susceptibility modelling and other spatial mapping tasks, and integrates seamlessly with INLA-based tools.

This paper introduces a novel unit-Lindley mixed-effects model (NULMM) within the generalized linear mixed model (GLMM) framework, designed for analyzing correlated response variables bounded within the unit interval. Parameter estimation was conducted via maximum likelihood, using Laplace approximation and adaptive Gaussian- Hermite quadrature (AGHQ). Simulation studies revealed that the Laplace approximation yielded biased estimates, while AGHQ with 5 or 11 quadrature points produced unbiased results. The proposed model was applied to rural electricity access data from South Asian countries, with covariates including time, log(GDP), log(Rural Population), and income level. Results show that time and log(GDP) are positively associated with rural electricity access, whereas log(Rural Population) has a negative association but is not statistically significant. Additionally, significant disparities were observed between low-income and upper-middle-income countries. Model comparisons demonstrated that NULMM provides a better fit to the data than the beta mixed model and the unit-Lindley (UL) mixed model.

Modeling block maxima using the generalized extreme value (GEV) distribution is a classical and widely used method for studying univariate extremes. It allows for theoretically motivated estimation of return levels, including extrapolation beyond the range of observed data. A frequently overlooked challenge in applying this methodology comes from handling datasets containing missing values. In this case, one cannot be sure whether the true maximum has been recorded in each block, and simply ignoring the issue can lead to biased parameter estimators and, crucially, underestimated return levels. We propose an extension of the standard block maxima approach to overcome such missing data issues. This is achieved by explicitly accounting for the proportion of missing values in each block within the GEV model. Inference is carried out using likelihood-based techniques, and we propose an update to commonly used diagnostic plots to assess model fit. We assess the performance of our method via a simulation study, with results that are competitive with the “ideal” case of having no missing values. The practical use of our methodology is demonstrated on sea surge data from Brest, France, and air pollution data from Plymouth, U.K.

The analysis of spatiotemporal data is fundamental across multiple scientific disciplines, particularly in assessing the behavior of climate effects over space and time. A key challenge in this area is effectively capturing recurring climate phenomena, such as El Niño/La Niña (ENSO) phases, which induce prolonged periods of similar weather patterns across affected regions. To address this, our study introduces a novel spatiotemporal regression model that explicitly incorporates block structures representing these recurring climate effects. These blocks accommodate ENSO phases and manage the within-block correlations and shared characteristics, enhancing the model's ability to capture the influence of such phenomena on precipitation variability. The model further integrates functional predictors of both fixed and random nature, along with spatial covariance modeled via the Matérn class, to accommodate complex spatial, temporal, and block-related structures. Motivated by a monthly precipitation dataset from meteorological stations in Goiás State, Brazil, spanning 21 years (1980–2001), our approach assigns spatial effects to individual stations, temporal effects to months, blocks to ENSO phases, and repeated measures to years within those blocks. The results from simulation studies demonstrate the model's robustness and effectiveness, providing deeper insight into how recurring climate effects like ENSO impact rainfall patterns. This framework represents a significant methodological advancement in spatiotemporal modeling, highlighting the importance of explicitly modeling and estimating the effects of recurrent climate phenomena through block structures.

Air pollution remains a major environmental risk factor that is often associated with adverse health outcomes. However, quantifying and evaluating its effects on human health is challenging due to the complex nature of exposure data. Recent technological advances have led to the collection of various indicators of air pollution at increasingly high spatial-temporal resolutions (e.g., daily averages of pollutant levels at spatial locations referenced by latitude-longitude). However, health outcomes are typically aggregated over several spatial-temporal coordinates (e.g., annual prevalence for a county) to comply with survey regulations. This article develops a Bayesian hierarchical model to analyze such spatially-temporally misaligned exposure and health outcome data. We develop Bayesian predictive stacking for spatially and temporally misaligned data to optimally combine inference from multiple predictive spatial-temporal models. Stacking allows us to avoid iterative estimation algorithms such as Markov chain Monte Carlo that struggle due to convergence issues inflicted by the presence of weakly identified parameters. We apply our proposed method to study the effects of ozone on asthma in the state of California.