Semi-Parametric Empirical Best Prediction for small area estimation of unemployment indicators

The Italian National Institute for Statistics regularly provides estimates of unemployment indicators using data from the Labor Force Survey. However, direct estimates of unemployment incidence cannot be released for Local Labor Market Areas. These are unplanned domains defined as clusters of municipalities; many are out-of-sample areas and the majority is characterized by a small sample size, which render direct estimates inadequate. The Empirical Best Predictor represents an appropriate, model-based, alternative. However, for non-Gaussian responses, its computation and the computation of the analytic approximation to its Mean Squared Error require the solution of (possibly) multiple integrals that, generally, have not a closed form. To solve the issue, Monte Carlo methods and parametric bootstrap are common choices, even though the computational burden is a non trivial task. In this paper, we propose a Semi-Parametric Empirical Best Predictor for a (possibly) non-linear mixed effect model by leaving the distribution of the area-specific random effects unspecified and estimating it from the observed data. This approach is known to lead to a discrete mixing distribution which helps avoid unverifiable parametric assumptions and heavy integral approximations. We also derive a second-order, bias-corrected, analytic approximation to the corresponding Mean Squared Error. Finite sample properties of the proposed approach are tested via a large scale simulation study. Furthermore, the proposal is applied to unit-level data from the 2012 Italian Labor Force Survey to estimate unemployment incidence for 611 Local Labor Market Areas using auxiliary information from administrative registers and the 2011 Census

A Fifty-Year Sustainability Assessment of Italian Agro-Forest Districts

DistrictAs cropland management and land use shifted towards more intensive practices, global land degradation increased drastically. Understanding relationships between ecological and socioeconomic drivers of soil and landscape degradation within these landscapes in economically dynamic contexts such as the Mediterranean region, requires multi-target and multi-scalar approaches covering long-term periods. This study provides an original approach for identifying desertification risk drivers and sustainable land management strategies within Italian agro-forest districts. An Environmental Sensitivity Area (ESA) approach, based on four thematic indicators (climate, soil, vegetation and land-use) and a composite index of desertification risk (ESAI), was used to evaluate changes in soil vulnerability and landscape degradation between the years 1960 and 2010. A multivariate model was developed to identify the most relevant drivers causing changes in land susceptibility at the district scale. Larger districts, and those with a higher proportion of their total surface area classified as agro-forest, had a significantly lower increase in land susceptibility to degradation during the 50 years when compared with the remaining districts. We conclude that preserving economic viability and ecological connectivity of traditional, extensive agricultural systems is a key measure to mitigate the desertification risk in the Mediterranean region

Finite mixtures of quantile and M-quantile regression models

In this paper we define a finite mixture of quan- tile and M-quantile regression models for heterogeneous and /or for dependent/clustered data. Components of the finite mixture represent clusters of individuals with homogeneous values of model parameters. For its flexibility and ease of estimation, the proposed approaches can be extended to ran- dom coefficients with a higher dimension than the simple random intercept case. Estimation of model parameters is obtained through maximum likelihood, by implementing an EM-type algorithm. The standard error estimates for model parameters are obtained using the inverse of the observed information matrix, derived through the Oakes (J R Stat Soc Ser B 61:479–482, 1999) formula in the M-quantile setting, and through nonparametric bootstrap in the quantile case. We present a large scale simulation study to analyse the practical behaviour of the proposed model and to evaluate the empiri- cal performance of the proposed standard error estimates for model parameters. We considered a variety of empirical set- tings in both the random intercept and the random coefficient case. The proposed modelling approaches are also applied to two well-known datasets which give further insights on their empirical behaviour

Bayesian Ideas in Survey Sampling: The Legacy of Basu

Survey sampling and, more generally, Official Statistics are experiencing an important renovation time. On one hand, there is the need to exploit the huge information potentiality that the digital revolution made available in terms of data. On the other hand, this process occurred simultaneously with a progressive deterioration of the quality of classical sample surveys, due to a decreasing willingness to participate and an increasing rate of missing responses. The switch from survey-based inference to a hybrid system involv- ing register-based information has made more stringent the debate and the possible resolution of the design-based versus model-based approaches con- troversy. In this new framework, the use of statistical models seems unavoid- able and it is today a relevant part of the official statistician toolkit. Models are important in several different contexts, from Small area estimation to non sampling error adjustment, but they are also crucial for correcting bias due to over and undercoverage of administrative data, in order to prevent potential selection bias, and to deal with different definitions and/or errors in the measurement process of the administrative sources. The progressive shift from a design-based to a model-based approach in terms of super-population is a matter of fact in the practice of the National Statistical Institutes. How- ever, the introduction of Bayesian ideas in official statistics still encounters difficulties and resistance. In this work, we attempt a non-systematic review of the Bayesian development in this area and try to highlight the extra ben- efit that a Bayesian approach might provide. Our general conclusion is that, while the general picture is today clear and most of the basic topics of survey sampling can be easily rephrased and tackled from a Bayesian perspective, much work is still necessary for the availability of a ready-to-use platform of Bayesian survey sampling in the presence of complex sampling design, non-ignorable missing data patterns, and large datasets

A probabilistic approach to risk assessment of slow slope movements

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lqmix: an R package for longitudinal data analysis via linear quantile mixtures

The analysis of longitudinal data gives the chance to observe how unit behaviors change over time, but it also poses series of issues. These have been the focus of a huge literature in the context of linear and generalized linear regression moving also, in the last ten years or so, to the context of linear quantile regression for continuous responses. In this paper, we present lqmix, a novel R package that helps estimate a class of linear quantile regression models for longitudinal data, in the presence of time-constant and/or time-varying, unit-specific, random coefficients, with unspecified distribution. Model parameters are estimated in a maximum likelihood framework, via an extended EM algorithm, and parameters' standard errors are estimated via a block-bootstrap procedure. The analysis of a benchmark dataset is used to give details on the package functions.Comment: 25 pages, 2 figure

The effectiveness of protection systems toward rockfall risk mitigation

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Ground Penetrating Radar Assessment of Flexible Road Pavement Degradation

GPR investigations were used to study degraded road pavements built in cutting sections. Road integrity was assessed via quantitative analysis of power curves. 1600 MHz and 600 MHz radar sections were collected in 40 damaged and undamaged road pavement sites. The collected data were processed as follows: (i) linearisation with regression analysis of power curves; (ii) assessment of absorption angle α′ which is directly proportional to absorption coefficient α (this was obtained by setting the e.m. propagation velocity to 10 cm/ns); (iii) comparison of absorption coefficients in both damaged and undamaged zones with respect to road pavement degradation. If the absorption coefficients of damaged and undamaged road sections have nearly the same value, then the likely cause of degradation is the fatigue or the thermal shrinkage; if they are not, then road degradation is due to the different compactness of the soil caused by vehicular traffic load. In a considerable number of sites, the statistical comparison of damaged and undamaged zones through the absorption coefficient analysis shows that surface observations of road pavements are quite consistent with power curve analyses

Bedside sonography assessment of extravascular lung water increase after major pulmonary resection in non-small cell lung cancer patients

Background: Extra vascular lung water (EVLW) following pulmonary resection increases due to fluid infusion and rises in capillary surface and permeability of the alveolar capillary membranes. EVLW increase clinically correlates to pulmonary oedema and it may generate impairments of gas exchanges and acute lung injury. An early and reliable assessment of postoperative EVLW, especially following major pulmonary resection, is useful in terms of reducing the risk of postoperative complications. The currently used methods, though satisfying these criteria, tend to be invasive and cumbersome and these factors might limit its use. The presence and burden of EVLW has been reported to correlate with sonographic B-line artefacts (BLA) assessed by lung ultrasound (LUS). This observational study investigated if bedside LUS could detect EVLW increases after major pulmonary resection. Due to the clinical association between EVLW increase and impairment of gas exchange, secondary aims of the study included investigating for associations between any observed EVLW increases and both respiratory ratio (PaO2/FiO2) and fluid retention, measured by brain natriuretic peptide (BNP). Methods: Overall, 74 major pulmonary resection patients underwent bedside LUS before surgery and at postoperative days 1 and 4, in the inviolate hemithorax which were divided into four quadrants. BLA were counted with a four-level method. The respiratory ratio PaO2/FiO2 and fluid retention were both assessed. Results: BLA resulted being increased at postoperative day 1 (OR 9.25; 95% CI, 5.28-16.20; P<0.0001 vs. baseline), and decreased at day 4 (OR 0.50; 95% CI, 0.31-0.80; P=0.004 vs. day 1). Moreover, the BLA increase was associated with both increased BNP (OR 1.005; 95% CI, 1.003-1.008; P<0.0001) and body weight (OR 1.040; 95% CI, 1.008-1.073; P=0.015). Significant inverse correlations were observed between the BLA values and the PaO2/FiO2 respiratory ratios. Conclusions: Our results suggest that LUS, due to its non-invasiveness, affordability and capacity to detect increases in EVLW, might be useful in better managing postoperative patients

Genetic diversity of dinitrogen-fixing bacterial communities in soil amended with olive husks

The industrial production of olive oil is accompanied by the accumulation of large quantities of by-products from the olive milling industry that are commonly dispersed as fertilisers, which are nowadays suspected to have potential toxic effects on is omicroflora. The aim of this work has been the investigation of the genetic diversity of bacterial communities present in soil treated with olive husks focusing on the dinitrogen-fixing bacteria.nifH genes were amplified from total soil DNA using universal primers, cloned and typed by restriction analysis and sequencing of representative haplotypes. On the same samples, DGGE analysis on amplified 16S rDNA was performed aiming at monitoring modifications in the total community pattern. Results showed a high genetic diversity ofnifH genes within the community, which was well in agreement with the total community profiles obtained by DGGE on 16SrDNA. Most of thenifH gene fragments (19 out of 32) were found to be similar to sequences related with clostridia