Firm-level training in local economic systems: Complementarities in production and firm innovation strategies

The paper presents a conceptual review over the main aspects concerning the role of human capital investments and training activities within production processes, followed by empirical evidence on two local economic systems in Northern Italy, based on recent survey data. Theoretical and empirical considerations are brought together in order to provide new insights on the role of training and the factors associated to training activities at firm level.The potential driving factors of training here analysed compounds structural characteristics, labour demand dynamics, human resource management practices, workforce features, and firm performances. We observe that training activities emerge positively associated with high-performance practices, innovative labour demand features, workforce skill level, firm size, and are affected by labour flexibility in various directions. Empirical evidence confirms most previous results but also adds further important insights. The analysis suggests that a widening gap, between few innovatively evolving and many stagnant firms, could characterise the future dynamics of the Region. The high relevance of structural variables, labour demand factors and HRM/innovation practices shows that regional industrial policies and labour policies should be jointly implemented for increasing potential firm productivity. This is a key concern for the current debate on local systems economic development in the European and Italian environment.     

PATTERN RECOGNITION VIA ROBUST SMOOTHING WITH APPLICATION TO LASER DATA

Nowadays airborne laser scanning is used in many territorial studies, providing point data which may contain strong discontinuities. Motivated by the need to interpolate such data and preserve their edges, this paper considers robust nonparametric smoothers. These estimators, when implemented with bounded loss functions, have suitable jump‐preserving properties. Iterative algorithms are developed here, and are equivalent to nonlinear M‐smoothers, but have the advantage of resembling the linear Kernel regression. The selection of their coefficients is carried out by combining cross‐validation and robust‐tuning techniques. Two real case studies and a simulation experiment confirm the validity of the method; in particular, the performance in building recognition is excellent.     

Earthquakes occurrences estimation through a parametric semi-Markov approach

The estimation of earthquakes’ occurrences prediction in seismic areas is a challenging problem in seismology and earthquake engineering. Indeed, the prevention and the quantification of possible damage provoked by destructive earthquakes are directly linked to this kind of prevision. In our paper, we adopt a parametric semi-Markov approach. This model assumes that a sequence of earthquakes is seen as a Markov process and besides it permits to take into consideration the more realistic assumption of events’ dependence in space and time. The elapsed time between two consecutive events is modeled as a general Weibull distribution. We determine then the transition probabilities and the so-called crossing states probabilities. We conclude then with a Monte Carlo simulation and the model is validated through a large database containing real data.     

Agent‐Based Recovery Model for Seismic Resilience Evaluation of Electrified Communities

In this article, an agent‐based framework to quantify the seismic resilience of an electric power supply system (EPSS) and the community it serves is presented. Within the framework, the loss and restoration of the EPSS power generation and delivery capacity and of the power demand from the served community are used to assess the electric power deficit during the damage absorption and recovery processes. Damage to the components of the EPSS and of the community‐built environment is evaluated using the seismic fragility functions. The restoration of the community electric power demand is evaluated using the seismic recovery functions. However, the postearthquake EPSS recovery process is modeled using an agent‐based model with two agents, the EPSS Operator and the Community Administrator. The resilience of the EPSS–community system is quantified using direct, EPSS‐related, societal, and community‐related indicators. Parametric studies are carried out to quantify the influence of different seismic hazard scenarios, agent characteristics, and power dispatch strategies on the EPSS–community seismic resilience. The use of the agent‐based modeling framework enabled a rational formulation of the postearthquake recovery phase and highlighted the interaction between the EPSS and the community in the recovery process not quantified in resilience models developed to date. Furthermore, it shows that the resilience of different community sectors can be enhanced by different power dispatch strategies. The proposed agent‐based EPSS–community system resilience quantification framework can be used to develop better community and infrastructure system risk governance policies.     

Predicting Children's Prosocial and Co‐operative Behavior from Their Temperamental Profiles: A Person‐centered Approach

The goal of this study was to examine how aspects of self‐regulation and negative emotionality predicted children's co‐operative and prosocial behavior concurrently and longitudinally using the National Institute of Child Health and Human Development (NICHD) Study of Early Child Care and Youth Development. Mothers completed measures of children's temperamental proneness to negative emotionality and self‐regulation at 54 months. Teachers and parents completed measures of children's co‐operative and prosocial behavior at 54 months, first grade, and third grade. A latent profile analysis of the temperamental variables revealed four profiles of children: those high in regulation and low in negative emotionality, those moderate in regulation and moderate in negative emotionality, those low in regulation and high in negative emotionality, and finally those who were very low in regulation but high in anger emotionality. Generally, children with profiles that were high or moderate in terms of regulation and low or moderate in terms of negative emotionality were rated as the most prosocial and co‐operative. Children with profiles that were less well regulated and who were high in negative emotionality (particularly anger proneness) were rated as less co‐operative and prosocial by parents and teachers.     

Predictors of Antisocial and Prosocial Behavior in an Adolescent Sports Context

This study examined antisocial and prosocial behavior of N = 439 adolescent athletes between 14 and 17 years of age (67 teams). Multi‐level analyses showed that team membership explained 20 and 13 percent of the variance in antisocial and prosocial behavior in the sports context, respectively. The team effects suggest that aggregating antisocial or prosocial adolescents within teams may partially explain differences in antisocial and prosocial behavior among athletes in the sports context. A trend was found toward a relation between higher levels of moral reasoning within teams, and less antisocial behavior in the sports context. Favorable moral atmosphere was positively associated with more prosocial behavior in the sports context. Finally, supportive coach–athlete relationships were associated with both less antisocial and more prosocial behavior in the sports context.     

Bayesian Conditional Mean Estimation in Log‐Normal Linear Regression Models with Finite Quadratic Expected Loss

Log‐normal linear regression models are popular in many fields of research. Bayesian estimation of the conditional mean of the dependent variable is problematic as many choices of the prior for the variance (on the log‐scale) lead to posterior distributions with no finite moments. We propose a generalized inverse Gaussian prior for this variance and derive the conditions on the prior parameters that yield posterior distributions of the conditional mean of the dependent variable with finite moments up to a pre‐specified order. The conditions depend on one of the three parameters of the suggested prior; the other two have an influence on inferences for small and medium sample sizes. A second goal of this paper is to discuss how to choose these parameters according to different criteria including the optimization of frequentist properties of posterior means.     

SIMPCA: a framework for rotating and sparsifying principal components

We propose an algorithmic framework for computing sparse components from rotated principal components. This methodology, called SIMPCA, is useful to replace the unreliable practice of ignoring small coefficients of rotated components when interpreting them. The algorithm computes genuinely sparse components by projecting rotated principal components onto subsets of variables. The so simplified components are highly correlated with the corresponding components. By choosing different simplification strategies different sparse solutions can be obtained which can be used to compare alternative interpretations of the principal components. We give some examples of how effective simplified solutions can be achieved with SIMPCA using some publicly available data sets.     

Analysis of the spread of Rhynchophorus ferrugineus in an urban area,using GIS techniques: a study case in Central Italy

Urban Ecosystems - The spread of the red palm weevil is now a very severe problem on a global scale. In this paper, we investigate its spread during the 2007–2013 period in a coastal urban...     

Model selection in high-dimensional noisy data: a simulation study

In many practical applications, high-dimensional regression analyses have to take into account measurement error in the covariates. It is thus necessary to extend regularization methods, that can handle the situation where the number of covariates p largely exceed the sample size n, to the case in which covariates are also mismeasured. A variety of methods are available in this context, but many of them rely on knowledge about the measurement error and the structure of its covariance matrix. In this paper, we set the goal to compare some of these methods, focusing on situations relevant for practical applications. In particular, we will evaluate these methods in setups in which the measurement error distribution and dependence structure are not known and have to be estimated from data. Our focus is on variable selection, and the evaluation is based on extensive simulations.