a b s t r a c tSixteen years of satellite radar altimeter data are analyzed to investigate the sea-level variation (SLV) of the Mediterranean Sea. The time evolution of the overall mean sea level of the Mediterranean Sea follows its own regional dynamics. The geographical distribution of the seasonal signal (annual and semi-annual) indicates that the major features of the Mediterranean Sea circulation are driving the highest seasonal variability, and that an eastward propagation exists between the western and eastern basins. While in previous studies the trend of SLV has been modeled as linear, in this study with a longer record of observations we found that a quadratic acceleration term is statistically significant for practically the whole basin, especially in those regions where the trend provides a significant contribution to the SLV. The inclusion of the quadratic acceleration term accounts better for the Mediterranean SLV trend, as the residual low frequency SLV in wintertime is highly correlated with NAO at zero time lag in almost the whole basin. The residual high-frequency signal variability, on the other hand, can be explained by mesoscale phenomena, such as eddies and gyres. Our comprehensive analysis of the Mediterranean SLV provides source observations for monitoring and understanding of both regular and transient phenomena.
In this paper we show how a simple model that captures user uncertainty can be used to define suitable measures of disclosure risk and data utility. The model generalizes previous results of Duncan and Lambert. 1 We present several examples to illustrate how the new measures can be used to implement existing optimality criteria for the choice of the best form of data release.
The problem of identifying potential determinants and predictors of dental caries is of key importance in caries research and it has received considerable attention in the scientific literature. From the methodological side, a broad range of statistical models is currently available to analyze dental caries indices (DMFT, dmfs, etc.). These models have been applied in several studies to investigate the impact of different risk factors on the cumulative severity of dental caries experience. However, in most of the cases (i) these studies focus on a very specific subset of risk factors; and (ii) in the statistical modeling only few candidate models are considered and model selection is at best only marginally addressed. As a result, our understanding of the robustness of the statistical inferences with respect to the choice of the model is very limited; the richness of the set of statistical models available for analysis in only marginally exploited; and inferences could be biased due the omission of potentially important confounding variables in the model's specification. In this paper we argue that these limitations can be overcome considering a general class of candidate models and carefully exploring the model space using standard model selection criteria and measures of global fit and predictive performance of the candidate models. Strengths and limitations of the proposed approach are illustrated with a real data set. In our illustration the model space contains more than 2.6 million models, which require inferences to be adjusted for ‘optimism'.
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