1,721,034 research outputs found
Alcuni aspetti dei momenti del secondo ordine di un processo di punto nel piano euclideo
No full textGranger causality analysis of bivariate climatic time series: a note on the role of CO2 emissions in global climate warming
No full textKriging with mixed effects models
No full textIn this paper the effectiveness of the use of mixed effects models for estimation and prediction purposes in spatial statistics for continuous data is reviewed in the classical and Bayesian frameworks. A case study on agricultural data is also provided
Kernel density estimation methods for assessing environmental risk surfaces in spatial case-control studies
No full textA hierarchical finite mixture model for Bayesian classification in the presence of auxiliary information
No full textGaussian finite-mixture models are extended to include the use of auxiliary information, the dependence of component membership probabilities being modelled by a generalized linear model for polytomous responses. Among the possible applications of the proposed methodology are probabilistic classification and estimation of group conditional parameters. Identifiability features of such a model are investigated in comparison with standard finite mixtures. A full Bayesian hierarchical representation of the model is developed to implement the Gibbs sampling estimation algorithm. Two examples are presented where the methodology is applied to the analysis of real and synthetic data
Sulla scelta delle variabili in un modello di regressione multipla: approcci computazionali bayesiani
No full textAssessment of disease risk in the presence of a putative environmental pollution source: a case study
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Finite mixture models for disease mapping: an application to the incidence of malignant mesothelioma in Apulia, Italy
No full textA data-set consisting of 112 residents in the 76 municipalities of the Departments of Bari and taranto (Apulia, Italy) diagnosed as suffering from malignant mesothelioma between 1990 and 1995 is analyzed. We show that finite mixture models are effective to produce a natural calssification of areas into a finite number risk levels without oversmoothing observed standardized incidence rates, as Hierarchical Bayesian methods do. Results are discussed in connection woth the past and present local industrial structure of the abive mentioned areas
Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA)
No full textAmong the many tools suited to detect local clusters in group-level data, Kulldorff–Nagarwalla’s spatial scan statistic gained wide popularity (Kulldorff and Nagarwalla in Stat Med 14(8):799–810, 1995). The underlying assumptions needed for making statistical inference feasible are quite strong, as counts in spatial units are assumed to be independent Poisson distributed random variables. Unfortunately, outcomes in spatial units are often not independent of each other, and risk estimates of areas that are close to each other will tend to be positively correlated as they share a number of spatially varying characteristics. We therefore introduce a Bayesian model-based algorithm for cluster detection in the presence of spatially autocorrelated relative risks. Our approach has been made possible by the recent development of new numerical methods based on integrated nested Laplace approximation, by which we can directly compute very accurate approximations of posterior marginals within short computational time (Rue et al. in JRSS B 71(2):319–392, 2009). Simulated data and a case study show that the performance of our method is at least comparable to that of Kulldorff–Nagarwalla’s statistic
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