In this article, we propose two methods for estimating space and space-time covariance functions from a Gaussian random field, based on the composite likelihood idea. The first method relies maximization of weighted version function, while second one is solution score equation. This last scheme quite general could be applied to any kind likelihood. An information criterion model selection estimation also introduced. are useful practitioners looking good balance between computational...

The statistical modeling of space-time extremes in environmental applications is key to understanding complex dependence structures original event data and generating realistic scenarios for impact models. In this context high-dimensional data, we propose a novel hierarchical model high threshold exceedances defined over continuous space time by embedding Gamma process convolution the rate an exponential variable, leading asymptotic independence time. Its physically motivated anisotropic...

Abstract In this study, we propose a time‐dependent susceptible‐exposed‐infected‐recovered (SEIR) model for the analysis of SARS‐CoV‐2 epidemic outbreak in three different countries, United States, Italy, and Iceland using public data inherent numbers wave. Since several types grades actions were adopted by governments, including travel restrictions, social distancing, or limitation movement, want to investigate how these measures can affect curve infectious population. The parameters...

Abstract Ensembles of meteorological quantities obtained from numerical models can be used for forecasting weather variables. Unfortunately, such ensembles are often biased and under-dispersed therefore need to post-processed. Ensemble model output statistics (EMOS) is a widely post-processing technique reduce bias dispersion errors ensembles. In the EMOS approach, full probabilistic prediction given in form predictive distribution with parameters depending on ensemble forecast members....

ABSTRACT This paper presents a hierarchical approach to modelling extremes of stationary time series. The procedure comprises two stages. In the first stage, exceedances over high threshold are modelled through generalized Pareto distribution, which is represented as mixture an exponential variable with Gamma distributed rate parameter. second latent process embedded inside distribution in order induce temporal dependence among exceedances. Unlike other extreme‐value models, this version has...

The loss of species that engage in close ecological interactions, such as pollination, has been shown to lead secondary extinctions, ultimately threatening the overall ecosystem stability and functioning. Pollination studies are currently flourishing at all possible levels interaction organization (i.e., species, guild, group network), different methodological protocols aimed define resilience pollination interactions have proposed. However, temporal dimension often overlooked. In light...

Summary We propose modelling short-term pollutant exposure effects on health by using dynamic generalized linear models. The time series of count data are modelled a Poisson distribution having mean driven latent Markov process; estimation is performed the extended Kalman filter and smoother. This strategy allows us to take into account possible overdispersion time-varying covariates. These ideas illustrated reanalysing relationship between daily non-accidental deaths air pollution in city...

Subset models are often useful in the analysis of stationary time series. Although subset autoregressive have received a lot attention, same attention has not been given to moving‐average (ARMA) models, as their identification can be computationally cumbersome. In this paper we propose overcome disadvantage by employing genetic algorithm. After encoding each ARMA model binary string, iterative algorithm attempts mimic natural evolution population such strings allowing reproduce, creating new...

Abstract This paper describes a framework for flexibly modeling zero‐inflated data. Semiparametric regression based on penalized splines Poisson models is introduced. Moreover, an EM‐type algorithm developed to perform maximum likelihood estimation. As illustration, study of animal abundance tackled. In fact, often shows excess zeroes and complicated function the explanatory variables. particular, relationships between avian environmental variables indicating land use are Copyright © 2007...

Abstract In this article, we use a transfer function‐noise (TFN) modelling strategy with single output and multiple/single inputs to study the relationships among large unconfined aquifer in upper Venetian plain (Northeast Italy), its recharge components (rainfalls losing river) multi‐layered confined system located middle plain. Model identification from data raises range of difficulties when seeking models consistent physical behaviour, but no information related function order lags zero...

Abstract In this article, we concentrate on an alternative modeling strategy for positive data that exhibit spatial or spatiotemporal dependence. Specifically, propose to consider stochastic processes obtained through a monotone transformation of scaled version χ 2 random processes. The latter is well known in the specialized literature and originates by summing independent copies squared Gaussian process. However, their use as models related inference has not been much considered. Motivated...

Invasive alien species risk assessment and adaptive management are often hindered by a lack of information for most species. This work aims at predicting the probability successful establishment invasion Oenothera stucchii Soldano, neophyte invasive belonging to sect. subsect. Oenothera, in xerophilous grasslands grey dunes. Based on fine-scale field data, we modelled O. presence/absence abundance as function environmental factors, human disturbance, attributes recipient community through...

The statistical modelling of discrete extremes has received less attention than their continuous counterparts in the extreme value theory (EVT) literature. One approach to transition from is threshold exceedances integer random variables by version generalized Pareto distribution. However, optimal choice thresholds defining remains a problematic issue. Moreover, regression framework, treatment majority non-extreme data below selected either ignored or separated extremes. To tackle these...

Journal Article A multiple‐imputation Metropolis version of the EM algorithm Get access Carlo Gaetan, Gaetan Search for other works by this author on: Oxford Academic Google Scholar Jian‐Feng Yao Biometrika, Volume 90, Issue 3, September 2003, Pages 643–654, https://doi.org/10.1093/biomet/90.3.643 Published: 01 2003