On the estimation of the Lorenz curve under complex sampling designs

Statistical Methods & Applications - This paper focuses on the estimation of the concentration curve of a finite population, when data are collected according to a complex sampling design with...     

Kinematic analysis of post office employees' workstations

This study analyzed a post office clerk's tasks, comparing two workstation models. The clerk was facing the client in one, and seated at 45 degrees to the counter in the other. We analyzed the most frequent tasks and those presenting the most critical points: 1) payment of a postal order; 2) accepting a registered letter, breaking them down into subtasks. We used an optoelectronic system for kinematic analysis, and calculated the range of motion of the trunk and arms in the three spatial planes. The 45( position required less torsion of the trunk and head when using the printer, placed to the left of the employee. A larger worktop improved the workstation, leaving more room for equipment and allowing the worker to sit frontally to the monitor. However, this solution involved a shorter distance between the worker and the client with longer extension of the shoulder and elbow and less trunk flexion. These findings suggested a modification in the layout that shortens the distance between the worker and client.     

Volunteer Motivation and Organizational Climate: Factors that Promote Satisfaction and Sustained Volunteerism in NPOs

Keeping volunteers committed and engaged is one of the toughest challenges for NPOs. The aim of the present study is to investigate the individual and organizational factors that promote volunteer satisfaction and, vice versa, foster intentions to quit. Two hundred forty-seven volunteers operating in four different NPOs were asked to fill in a self-report questionnaire that aimed to explore their motivations to volunteer, their degree of satisfaction and their perception of the organizational climate in the NPO they worked with, in addition to providing details of the activities which they were involved in. Results showed that the organizational climate mediates the relationship between autonomous motivation and satisfaction, as well as that between external motivation and intentions to leave an organization.     

The Photovoltaic Market Facing the Challenge of Organic Solar Cells: Economic and Technical Perspectives

Solar cells are gaining more interest in recent times as a possible technological solution to improve energy production whilst reducing its environmental impact. The majority of the market is still dominated by first generation solar cells made of single crystal or poly-crystalline silicon solar cells. Silicon technology has reached an high standard efficiency. However, silicon technology needs a very pure material that means high cost in dollar per watt. In order to improve the production of solar power, governments have started big economical interventions, like the feed-in tariff. The advancement of technology, especially the nano-technological revolution, is bringing the attention to another generation of solar cells, hybrid/organic solar cells like Dye Solar Cells, which reduce the costs and suggest the possibility of a mass production of solar modules without the need of public funds.     

Wild adaptive trimming for robust estimation and cluster analysis

Trimming principles play an important role in robust statistics. However, their use for clustering typically requires some preliminary information about the contamination rate and the number of groups. We suggest a fresh approach to trimming that does not rely on this knowledge and that proves to be particularly suited for solving problems in robust cluster analysis. Our approach replaces the original K‐population (robust) estimation problem with K distinct one‐population steps, which take advantage of the good breakdown properties of trimmed estimators when the trimming level exceeds the usual bound of 0.5. In this setting, we prove that exact affine equivariance is lost on one hand but, on the other hand, an arbitrarily high breakdown point can be achieved by “anchoring” the robust estimator. We also support the use of adaptive trimming schemes, in order to infer the contamination rate from the data. A further bonus of our methodology is its ability to provide a reliable choice of the usually unknown number of groups.     

Snipping for robust k-means clustering under component-wise contamination

We introduce the concept of snipping, complementing that of trimming, in robust cluster analysis. An observation is snipped when some of its dimensions are discarded, but the remaining are used for clustering and estimation. Snipped k-means is performed through a probabilistic optimization algorithm which is guaranteed to converge to the global optimum. We show global robustness properties of our snipped k-means procedure. Simulations and a real data application to optical recognition of handwritten digits are used to illustrate and compare the approach.     

Joint analysis of occurrence and time to stability after entrance into the Italian labour market: an approach based on a Bayesian cure model with structured stochastic search variable selection

Precarious employment is a serious social problem, especially in those countries, such as Italy, where there are limited benefits from social security. We investigate this phenomenon by analysing the initial part of the career of employees starting with unstable contracts for a panel of Italian workers. Our aim is to estimate the probability of getting a stable job and to detect factors influencing both this probability and the duration of precariousness. To answer these questions, we use an ad hoc mixture cure rate model in a Bayesian framework.     

A Dominance-Based Rough Set Approach for an Enhanced Assessment of Seasonal Influenza Risk

Accounting for about 290,000–650,000 deaths across the globe, seasonal influenza is estimated by the World Health Organization to be a major cause of mortality. Hence, there is a need for a reliable and robust epidemiological surveillance decision-making system to understand and combat this epidemic disease. In a previous study, the authors proposed a decision support system to fight against seasonal influenza. This system is composed of three subsystems: (i) modeling and simulation, (ii) data warehousing, and (iii) analysis. The analysis subsystem relies on spatial online analytical processing (S-OLAP) technology. Although the S-OLAP technology is useful in analyzing multidimensional spatial data sets, it cannot take into account the inherent multicriteria nature of seasonal influenza risk assessment by itself. Therefore, the objective of this article is to extend the existing decision support system by adding advanced multicriteria analysis capabilities for enhanced seasonal influenza risk assessment and monitoring. Bearing in mind the characteristics of the decision problem considered in this article, a well-known multicriteria classification method, the dominance-based rough set approach (DRSA), was selected to boost the existing decision support system. Combining the S-OLAP technology and the multicriteria classification method DRSA in the same decision support system will largely improve and extend the scope of analysis capabilities. The extended decision support system has been validated by its application to assess seasonal influenza risk in the northwest region of Algeria.     

Quantile regression for longitudinal data based on latent Markov subject-specific parameters

We propose a latent Markov quantile regression model for longitudinal data with non-informative drop-out. The observations, conditionally on covariates, are modeled through an asymmetric Laplace distribution. Random effects are assumed to be time-varying and to follow a first order latent Markov chain. This latter assumption is easily interpretable and allows exact inference through an ad hoc EM-type algorithm based on appropriate recursions. Finally, we illustrate the model on a benchmark data set.