A class of high-breakdown scale estimators based on subranges

We consider a new class of scale estimators with 50% breakdown point. The estimators are defined as order statistics of certain subranges. They all have a finite-sample breakdown point of [n/2]/n, which is the best possible value. (Here, [...] denotes the integer part.) One estimator in this class has the same influence function as the median absolute deviation and the least median of squares (LMS) scale estimator (i.e., the length of the shortest half), but its finite-sample efficiency is higher. If we consider the standard deviation of a subsample instead of its range, we obtain a different class of 50% breakdown estimators. This class contains the least trimmed squares (LTS) scale estimator. Simulation shows that the LTS scale estimator is nearly unbiased, so it does not need a small-sample correction factor. Surprisingly, the efficiency of the LTS scale estimator is less than that of the LMS scale estimator.     

The impact of education on third births. A multilevel discrete-time hazard analysis

We propose to use multilevel discrete-time hazard models to assess the impact of societal and individual level covariates on the timing and occurrence of third births. We focus mainly on the impact of educational attainment on third births across 15 European countries. From the analysis in this paper, the effect of education on the propensity to have a third child is found to be negative. This education effect is not significantly weakened by the Nordic countries, but living in Scandinavia does increase the hazard for a third birth.     

Personal development plans supporting employee learning and perceived performance: the moderating role of self-directedness

Due to developments in the current dynamic business environment which is more global and competitive than ever, employees’ lifelong learning and the way organizations support their continuous development are pivotal. Many organizations introduce personal development plans (PDPs) to support employees’ learning, however, not always taking into account their personal characteristics. This study had a twofold purpose as it was designed to explore on the one hand the relationship between PDPs in the workplace with employee’s undertaking of learning activities and perceived performance, and on the other hand the possible moderating role of employee self-directedness on this relationship. For this study, data collected by means of a questionnaire at two different organizations were used, one organization foregoing profit and one non-profit activity. The results indicated significant positive relations between the individual supporting factor ‘employee learning and reflection’ and the undertaking of learning activities and perceived performance and between employee self-directedness and the undertaking of learning activities and perceived performance. The same accounted for the moderating effect of employee self-directedness on the interaction between learning and reflection and perceived performance. Some findings were surprising, nevertheless resulted in valuable insights and implications for practice.     

Influence functions of the Spearman and Kendall correlation measures

Nonparametric correlation estimators as the Kendall and Spearman correlation are widely used in the applied sciences. They are often said to be robust, in the sense of being resistant to outlying observations. In this paper we formally study their robustness by means of their influence functions and gross-error sensitivities. Since robustness of an estimator often comes at the price of an increased variance, we also compute statistical efficiencies at the normal model. We conclude that both the Spearman and Kendall correlation estimators combine a bounded and smooth influence function with a high efficiency. In a simulation experiment we compare these nonparametric estimators with correlations based on a robust covariance matrix estimator.     

The Good,the Bad and the Ugly: Discourse,Policy Controversies and the Role of Science in the Politics of Shrimp Farming Development

This article revisits through a policy analysis the ongoing debate on shrimp farming aquaculture. It describes the changes in policy orientations that have taken place in recent years, and tries to relate them to the advocacy strategies developed by different networks and policy communities. The analysis reveals in particular the crucial contribution of the ‘power of expertise’ and shows how it has been instrumentalised by certain advocacy networks to depoliticise the debate. While this has allowed a number of key stakeholders to refocus the debate on technical solutions, it has prevented other groups concerned with more intractable social and political issues from engaging successfully in the policy process, thus leaving the long‐term sustainability of aquaculture still a contentious issue.     

An online classification EM algorithm based on the mixture model

Mixture model-based clustering is widely used in many applications. In certain real-time applications the rapid increase of data size with time makes classical clustering algorithms too slow. An online clustering algorithm based on mixture models is presented in the context of a real-time flaw-diagnosis application for pressurized containers which uses data from acoustic emission signals. The proposed algorithm is a stochastic gradient algorithm derived from the classification version of the EM algorithm (CEM). It provides a model-based generalization of the well-known online k-means algorithm, able to handle non-spherical clusters. Using synthetic and real data sets, the proposed algorithm is compared with the batch CEM algorithm and the online EM algorithm. The three approaches generate comparable solutions in terms of the resulting partition when clusters are relatively well separated, but online algorithms become faster as the size of the available observations increases.