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Summary Unemployment rate estimates for small areas are used to efficiently support the distribution of services and the allocation of resources, grants and funding. A Fay–Herriot type model is the most used tool to obtain these estimates. Under this approach out‐of‐sample areas require some synthetic estimates. As the geographical context is extremely important for analysing local economies, in this paper, we allow for area random effects to be spatially correlated. The spatial model parameters are estimated by a marginal likelihood method and are used to predict in‐sample as well as out‐of‐sample areas. Extensive simulation experiments are used to assess the impact of the auto‐regression parameter and of the rate of out‐of‐sample areas on the performance of this approach. The paper concludes with an illustrative application on real data from the Italian Labour Force Survey in which the estimation of the unemployment rate in each Local Labour Market Area is addressed. [ABSTRACT FROM AUTHOR]

Maximum likelihood estimation of the model parameters for a spatial population based on data collected from a survey sample is usually straightforward when sampling and non‐response are both non‐informative, since the model can then usually be fitted using the available sample data, and no allowance is necessary for the fact that only a part of the population has been observed. Although for many regression models this naive strategy yields consistent estimates, this is not the case for some models, such as spatial auto‐regressive models. In this paper, we show that for a broad class of such models, a maximum marginal likelihood approach that uses both sample and population data leads to more efficient estimates since it uses spatial information from sampled as well as non‐sampled units. Extensive simulation experiments based on two well‐known data sets are used to assess the impact of the spatial sampling design, the auto‐correlation parameter and the sample size on the performance of this approach. When compared to some widely used methods that use only sample data, the results from these experiments show that the maximum marginal likelihood approach is much more precise. [ABSTRACT FROM AUTHOR]

Technological progress has widely been acknowledged as a key driver in the achievement of environmental sustainability, and a new concern has recently arisen for assessing the role of differences in regional innovation performances with respect to the pursuit of pollution targets. We investigate the impact of innovative activities on environmental efficiency at the regional level, adopting a spatial panel data approach to account for dependence between geographical observations and to get deeper insights into spatial knowledge spillovers arising from agglomeration economies. The spatial panel analysis covers 103 Italian provinces (equivalent to NUTS3 regions) on four major air pollutants (i.e., CO2, CH4, NMVOC, CO) over the 1995–2010 period. Our findings show that the state of local technology development, proxied by patents, has a positive impact on GDP emission efficiency and that its magnitude is higher for more localized pollutants as a result of larger spillover effects. Finally, to analyze the Italian North‐South dualism of emission levels of pollutants, we offer a spillover effect study to emphasize the remarkable difference among effects in the territory. This calls for closer attention to the local relevance of the pollution damage when environmental impact of innovation strategies is to be evaluated. [ABSTRACT FROM AUTHOR]

Geographical data in economic, social or environmental sciences are usually recorded as compositions, i.e. relative frequencies, and a common inquiring problem concerns the analysis of these data over different geographical regions. In the present paper we define a new statistical test to verify spatial dependence of such geographical distributions based on distance correlation, a recently introduced measure of dependence between random vectors. The proposed index computes the non‐linear spatial distance between distributions and can be applied on compositional frequency distributions. Simulations and an application on Italian electoral data are presented, to illustrate the capabilities of the proposed test to detect spatial dependence. [ABSTRACT FROM AUTHOR]

Spatially distributed data exhibit particular characteristics that should be considered when designing a survey of spatial units. Unfortunately, traditional sampling designs generally do not allow for spatial features, even though it is usually desirable to use information concerning spatial dependence in a sampling design. This paper reviews and compares some recently developed randomised spatial sampling procedures, using simple random sampling without replacement as a benchmark for comparison. The approach taken is design-based and serves to corroborate intuitive arguments about the need to explicitly integrate spatial dependence into sampling survey theory. Some guidance for choosing an appropriate spatial sampling design is provided, and some empirical evidence for the gains from using these designs with spatial populations is presented, using two datasets as illustrations. [ABSTRACT FROM AUTHOR]

The units observed in a biological, agricultural, and environmental survey are often randomly selected from a finite population whose main feature is to be geo-referenced thus its spatial distribution should be used as essential information in designing the sample. In particular our interest is focused on probability samples that are well spread over the population in every dimension which in recent literature are defined as spatially balanced samples. To approach the problem we used the within sample distance as the summary index of the spatial distribution of a random selection criterion. Moreover numerical comparisons are made between the relative efficiency, measured with respect to the simple random sampling, of the suggested design and some other classical solutions as the Generalized Random Tessellation Stratified (GRTS) design used by the US Environmental Protection Agency (EPA) and other balanced or spatially balanced selection procedures as the Spatially Correlated Poisson Sampling (SCPS), the balanced sampling (CUBE), and the Local Pivotal method (LPM). These experiments on real and simulated data show that the design based on the within sample distance selects samples with a better spatial balance thus gives estimates with a lower sampling error than those obtained by using the other methods. The suggested method is very flexible to the introduction of stratification and coordination of samples and, even if in its nature it is computationally intensive, it is shown to be a suitable solution even when dealing with high sampling rates and large population frames where the main problem arises from the size of the distance matrix. [ABSTRACT FROM AUTHOR]

In the last year, a central issue in regional economic growth debate has been represented by the empirical analysis of Verdoorn's law related to the long-term dynamic relationship between the rate of growth in output and the productivity growth due to increasing returns. Several papers have tested Verdoorn's law on European countries as well as many other world economies. Recently, attempts have been made to provide foundations for a spatial version of the original law specification. The main contributions were dedicated to the inclusion of spatial dependence in the economic model. Surprisingly, in the literature on Verdoom's law the analysis of the spatial heterogeneity is not often considered. The aim of this paper is the regional analysis of the spatial dependence and heterogeneity in Verdoorn's law, identifying spatial regimes that can be interpreted as clusters of productivity growth in European regions at NUTS 2 level. To pursue this objective, an optimization algorithm for the identification of groups is used. This constitutes a modified version of Simulated Annealing. [ABSTRACT FROM AUTHOR]

The article presents a study which focused on a logistic auto-logistic model (LAM) for the estimation of the univariate conditional probabilities of flood events. An analysis of the estimation and simulation techniques is also presented, focusing on the pseudo-maximum likelihood estimation (MLE) procedure and on the Gibbs Sampler. The empirical analysis of the section on the estimation of the conditional distributions, is mainly devoted to the coarser geographical partition described by the so-called macro-system.

Reactive oxygen and nitrogen species (RONS) can be generated in liquids by different cold plasma processes. These species can influence cell biology and pathological states, and trigger wound healing, disinfection, and cancer treatments. The optimization of plasma sources' configurations and experimental conditions is fundamental to dose‐produced RONS in liquids. This research is a case study comparing RONS generation (H2O2 and NO2−) in water, after exposure to two different atmospheric pressure cold dielectric barrier discharge (DBD) plasma sources: a plasma jet‐DBD and a planar‐DBD operating in contact with the liquid and in remote, respectively. The data demonstrate that the reactor geometry and the treatment modality strongly impact the generation of the active species in water. [ABSTRACT FROM AUTHOR]

The article highlights the International Statistical Review which is the journal of the International Statistical Institute and its Associations; includes list of editorial members such as Narayanaswamy Balakrishnan, Roberto Benedetti, and James Brown; and lists the contents related to journal.

Acknowledgement is presented from Environmetrics journal of the efforts of the individuals who served as anonymous referees from November 26, 2014 through November 26, 2015.

Intra-arterial transplantation of mesoangioblasts proved safe and partially efficacious in preclinical models of muscular dystrophy. We now report the first-in-human, exploratory, non-randomized open-label phase I-IIa clinical trial of intra-arterial HLA-matched donor cell transplantation in 5 Duchenne patients. We administered escalating doses of donor-derived mesoangioblasts in limb arteries under immunosuppressive therapy (tacrolimus). Four consecutive infusions were performed at 2-month intervals, preceded and followed by clinical, laboratory, and muscular MRI analyses. Two months after the last infusion, a muscle biopsy was performed. Safety was the primary endpoint. The study was relatively safe: One patient developed a thalamic stroke with no clinical consequences and whose correlation with mesoangioblast infusion remained unclear. MRI documented the progression of the disease in 4/5 patients. Functional measures were transiently stabilized in 2/3 ambulant patients, but no functional improvements were observed. Low level of donor DNA was detected in muscle biopsies of 4/5 patients and donor-derived dystrophin in 1. Intra-arterial transplantation of donor mesoangioblasts in human proved to be feasible and relatively safe. Future implementation of the protocol, together with a younger age of patients, will be needed to approach efficacy. [ABSTRACT FROM AUTHOR]