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A projection-based Laplace approximation for spatial latent variable models

期刊: ENVIRONMETRICS, 2022; 33 (1)

Laplace method is a practical tool for obtaining maximum likelihood estimators for a wide class of latent variable models. The main idea is to approxi......

JIF:1.875

Quantile based modeling of diurnal temperature range with the five-parameter lambda distribution

期刊: ENVIRONMETRICS, 2022; 33 (4)

Diurnal temperature range is an important variable in climate science that can provide information regarding climate variability and climate change. C......

JIF:1.875

Two-phase adaptive cluster sampling with circular field plots

期刊: ENVIRONMETRICS, 2022; 33 (5)

Adaptive cluster sampling (ACS) is extended to the case when the primary sampling units consist of circular field plots. When conducting field work fo......

JIF:1.875

A Dirichlet process model for change-point detection with multivariate bioclimatic data

期刊: ENVIRONMETRICS, 2022; 33 (1)

Motivated by real-world data of monthly values of precipitation, minimum, and maximum temperature recorded at 360 monitoring stations covering the Ita......

JIF:1.875

Mitigating spatial confounding by explicitly correlating Gaussian random fields

期刊: ENVIRONMETRICS, 2022; 33 (5)

Spatial models are used in a variety of research areas, such as environmental sciences, epidemiology, or physics. A common phenomenon in such spatial ......

JIF:1.875

Discussion on A combined estimate of global temperature

期刊: ENVIRONMETRICS, 2022; 33 (3)

While the real-world has warmed in one unique way, the available data, which is spatio-temporally incomplete and contains biases of unknown nature and......

JIF:1.875

Clustering of bivariate satellite time series: A quantile approach

期刊: ENVIRONMETRICS, 2022; 33 (7)

Clustering has received much attention in statistics and machine learning with the aim of developing statistical models and autonomous algorithms whic......

JIF:1.875

Recognizing a spatial extreme dependence structure: A deep learning approach

期刊: ENVIRONMETRICS, 2022; 33 (4)

Understanding the behavior of extreme environmental events is crucial for evaluating economic losses, assessing risks, and providing health care, amon......

JIF:1.875

A notable Gamma-Lindley first-order autoregressive process: An application to hydrological data

期刊: ENVIRONMETRICS, 2022; 33 (4)

A new Gamma-Lindley (GaL) first-order autoregressive process (AR) is introduced, called GaL-AR(1). The distribution for the GaL-AR(1) innovation proce......

JIF:1.875

Estimation of the spatial weighting matrix for regular lattice data-An adaptive lasso approach with cross-sectional resampling

期刊: ENVIRONMETRICS, 2022; 33 (1)

Spatial autoregressive models typically rely on the assumption that the spatial dependence structure is known in advance and is represented by a deter......

JIF:1.875

Generalization of the power-law rating curve using hydrodynamic theory and Bayesian hierarchical modeling

期刊: ENVIRONMETRICS, 2022; 33 (2)

The power-law rating curve has been used extensively in hydraulic practice and hydrology. It is given by Q(h)=a(h-c)b, where Q is discharge, h is wate......

JIF:1.875

Modeling the spatial evolution wildfires using random spread process

期刊: ENVIRONMETRICS, 2022; 33 (8)

The study of wildfire spread and the growth of the area burned is an important task in ecological studies and in other contexts. In this work we prese......

JIF:1.875

A spatiotemporal analysis of NO2 concentrations during the Italian 2020 COVID-19 lockdown

期刊: ENVIRONMETRICS, 2022; 33 (4)

When a new environmental policy or a specific intervention is taken in order to improve air quality, it is paramount to assess and quantify-in space a......

JIF:1.875

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