Annalina Sarra

Hierarchical generalised latent spatial quantile regression models with applications to indoor radon concentration

Radon-222 is a noble gas arising naturally from decay of uranium-238 present in the earth’s crust. In confined spaces, high concentrations of radon can become a serious health concern. Hence, experts widely agree that prolonged exposure to this gas can significantly increase the risk of lung cancer. A range of variables, such as geological factors, soil properties, building characteristics, the living habits of dwellers and meteorological parameters, might have a significant impact on indoor radon concentration and its variability. In this paper, the effect of various factors that are believed to influence the indoor radon concentrations is studied at the municipal level of L’Aquila district (Abruzzo region, Italy). The statistical analysis is carried out through a hierarchical Bayesian spatial quantile regression model in which the matrix of explanatory variables is partially defined through a set of spatial common latent factors. The proposed model, here referred to as the Generalized latent-spatial-quantile regression model, is thus appropriate when some covariates are indicators of latent factors that can be used as predictors in the quantile regression and the variables are supposed to be spatially correlated. It is shown that the model has an intuitive appeal and that it is preferable when the interest is in studying the effects of covariates on one or both the tails of the response distribution, as in the case of indoor radon concentrations. Full probabilistic inference is performed by applying Markov chain Monte Carlo techniques.

Residential radon concentration in the Abruzzo region (Italy): a different perspective for identifying radon prone areas

Indoor radon is an important risk factor for human health. Indeed radon inhalation is considered the second cause of lung cancer after smoking. During the last decades, in many countries huge efforts have been made in order to measuring, mapping and predicting radon levels in dwellings. Various researches have been devoted to identify those areas within the country where high radon concentrations are more likely to be found. Data collected through indoor radon surveys have been analysed adopting various statistical approaches, among which hierarchical Bayesian models and geostatistical tools are worth noting. The essential goal of this paper regards the identification of high radon concentration areas (the so-called radon prone areas) in the Abruzzo Region (Italy). In order to accurately pinpoint zones deserving attention for mitigation purpose, we adopt spatial cluster detection techniques, traditionally employed in epidemiology. As a first step, we assume that indoor radon measurements do not arise from a continuous spatial process; thus the geographic locations of dwellings where the radon measurements have been taken can be viewed as a realization of a spatial point process. Following this perspective, we adopt and compare recent cluster detection techniques: the simulated annealing scan statistic, the case event approach based on distance regression on the selection order and the elliptic spatial scan statistic. The analysis includes data collected during surveys carried out by the Regional Agency for the Environment Protection of Abruzzo (ARTA) in 1,861 random sampled dwellings across 277 municipalities of the Abruzzo region. The radon prone areas detected by the selected approaches are provided along with the summary statistics of the methods. Finally, the methodologies considered in this paper are tested on simulated data in order to evaluate their power and the precision of cluster location detection.

The Application of M-Function Analysis to the Geographical Distribution of Earthquake Sequence

Seismicity is a complex phenomenon and its statistical investigation is mainly concerned with the developing of computational models of earthquake processes. However, a substantial number of studies have been performed on the distribution of earthquakes in space and time in order to better understand the earthquake generation process and improve its prediction. The objective of the present paper, is to explore the effectiveness of a variant of Ripley’s K-function, the M-function, as a new means of quantifying the clustering of earthquakes. In particular we test how the positions of epicentres are clustered in space with respect to their attributes values, i.e. the magnitude of the earthquakes. The strength of interaction between events is discussed and results for L’Aquila earthquake sequence are analysed.

Quantile regression and Bayesian cluster detection to identify radon prone areas

Albeit the dominant source of radon in indoor environments is the geology of the territory, many studies have demonstrated that indoor radon concentrations also depend on dwelling-specific characteristics. Following a stepwise analysis, in this study we propose a combined approach to delineate radon prone areas. We first investigate the impact of various building covariates on indoor radon concentrations. To achieve a more complete picture of this association, we exploit the flexible formulation of a Bayesian spatial quantile regression, which is also equipped with parameters that controls the spatial dependence across data. The quantitative knowledge of the influence of each significant building-specific factor on the measured radon levels is employed to predict the radon concentrations that would have been found if the sampled buildings had possessed standard characteristics. Those normalised radon measures should reflect the geogenic radon potential of the underlying ground, which is a quantity directly related to the geological environment. The second stage of the analysis is aimed at identifying radon prone areas, and to this end, we adopt a Bayesian model for spatial cluster detection using as reference unit the building with standard characteristics. The case study is based on a data set of more than 2000 indoor radon measures, available for the Abruzzo region (Central Italy) and collected by the Agency of Environmental Protection of Abruzzo, during several indoor radon monitoring surveys.

Interpreting Error Measurement: A Case Study Based on Rasch Tree Approach

This paper describes the appropriateness of Differential Item Functioning (DIF) analysis performed via mixed-effects Rasch models. Groups of subjects with homogeneous Rasch item parameters are found automatically by a model-based partitioning (Rasch tree model). The unifying framework offers the advantage of including the terminal nodes of Rasch tree in the multilevel formulation of Rasch models. In such a way we are able to handle different measurement issues. The approach is illustrated with a cross-national survey on attitude towards female stereotypes. Evidence of groups DIF was detected and presented as well as the estimates of model parameters.

A simulation study on the hybrid nature of Tango's index

Since the early 1990s, there has been an increasing interest in statistical methods for detecting global spatial clustering in data sets. Tangos index is one of the most widely used spatial statistics for assessing whether spatially distributed disease rates are independent or clustered. Interestingly, this statistic can be partitioned into the sum of two terms: one term is similar to the usual chi-square statistic, being based on deviation patterns between the observed and expected values, and the other term, similar to Morans I, is able to detect the proximity of similar values. In this paper, we examine this hybrid nature of Tangos index. The goal is to evaluate the possibility of distinguishing the spatial sources of clustering: lack of fit or spatial autocorrelation. To comply with the aims of the work, a simulation study is performed, by which examples of patterns driving the goodness-of-fit and spatial autocorrelation components of the statistic are provided. As for the latter aspect, it is worth noting that inducing spatial association among count data without adding lack of fit is not an easy task. In this respect, the overlapping sums method is adopted. The main findings of the simulation experiment are illustrated and a comparison with a previous research on this topic is also highlighted.

Radon Level in Dwellings and Uranium Content in Soil in the Abruzzo Region: A Preliminary Investigation by Geographically Weighted Regression

Radon is a noble gas coming from the natural decay of uranium. It can migrate from the underlying soil into buildings, where sometimes very high concentration can be found, particularly in the basement or at ground floor. It contributes up to about the 50% of the ionizing radiation dose received by the population, constituting a real health hazard. In this study, we use the geographically weighted regression (GWR) technique to detect spatial non-stationarity of the relationship between indoor radon concentration and the radioactivity content of soil in the Provincia of L’Aquila, in the Abruzzo region (Central Italy). Radon measurements have been taken in a sample of 481 dwellings. Local estimates are obtained and discussed. The significance of the spatial variability in the local parameter estimates is examined by performing a Monte Carlo test.