Robust estimation of the mean vector for high-dimensional data set using robust clustering

The first step in statistical analysis is the parameter estimation. In multivariate analysis, one of the parameters of interest to be estimated is the mean vector. In multivariate statistical analysis, it is usually assumed that the data come from a multivariate normal distribution. In this situation, the maximum likelihood estimator (MLE), that is, the sample mean vector, is the best estimator. However, when outliers exist in the data, the use of sample mean vector will result in poor estimation. So, other estimators which are robust to the existence of outliers should be used. The most popular robust multivariate estimator for estimating the mean vector is S-estimator with desirable properties. However, computing this estimator requires the use of a robust estimate of mean vector as a starting point. Usually minimum volume ellipsoid (MVE) is used as a starting point in computing S-estimator. For high-dimensional data computing, the MVE takes too much time. In some cases, this time is so large that the existing computers cannot perform the computation. In addition to the computation time, for high-dimensional data set the MVE method is not precise. In this paper, a robust starting point for S-estimator based on robust clustering is proposed which could be used for estimating the mean vector of the high-dimensional data. The performance of the proposed estimator in the presence of outliers is studied and the results indicate that the proposed estimator performs precisely and much better than some of the existing robust estimators for high-dimensional data.     

A robust R control chart based on a two-step estimator of the process dispersion

ABSTRACT

Control charts are the frequently used tools for monitoring and controlling the processes. Classical control charts are sensitive to existing contaminated data which may be presented in the data collected from the processes. Thus, these charts are not able to control the processes precisely when the data are contaminated. Robust control charts are those which are less sensitive to contamination. Some robust control charts for monitoring the process variability were proposed in the past which are robust to some sorts of contamination. In this paper a new robust R control chart is proposed which is less sensitive to wide range of contaminations, i.e. general and local contaminations. Simulation studies are performed to compare the performance of the proposed control chart with some classical and robust control charts, using ARL and MSD as criteria for comparisons purposes. The simulation results show a very good performance of the proposed chart when both types of contaminations exist.     

Maternity Management in SMEs: A Transdisciplinary Review and Research Agenda

This paper provides a transdisciplinary critical review of the literature on maternity management in small and medium‐sized enterprises (SMEs), embedded within the wider literatures on maternity in the workplace. The key objectives are to describe what is known about the relations that shape maternity management in smaller workplaces and to identify research directions to enhance this knowledge. The review is guided by theory of organizational gendering and small business management, conceptualizing adaptions to maternity as a process of mutual adjustment and dynamic capability within smaller firms’ informally negotiated order, resource endowments and wider labour and product/service markets. A context‐sensitive lens is also applied. The review highlights the complex range of processes involved in SME maternity management and identifies major research gaps in relation to pregnancy, maternity leave and the return to work (family‐friendly working and breastfeeding) in these contexts. This blind spot is surprising, as SMEs employ the majority of women worldwide. A detailed agenda for future research is outlined, building on the gaps identified by the review and founded on renewed theoretical direction.     

Women Scientists and Engineers in European Companies: Putting Motherhood under the Microscope

Unlike the rise in women's participation in other professional sectors, women still form a minority of professional scientists and engineers, especially in multinational companies. Moreover, embedded gendered cultures in the science, engineering and technology (SET) sectors continue to affect the career progression of professional women, with few women reaching senior management positions and many leaving and failing to return. This article examines the experiences of women SET professionals in three European companies based in France, The Netherlands and Italy and illustrates how the careers of SET professionals in industry are shaped not only by corporate cultures and practices but also by the specific national contexts in which they live and work. In particular, we look at how motherhood rather than gender alone is constructed as problematic and propose a model of strategies that women adopt in doing motherhood and SET, including assimilation, cul‐de‐sac, breaking the mould and lying low.     

‘You Can Stand on Your Head and Still End Up with Lower Pay’: Gliding Segregation and Gendered Work Practices in Danish ‘Family‐friendly’ Workplaces

This article explores the process of gliding segregation in two Danish workplaces. We address the questions of how and why women and men at the same workplace, with the same levels of education, often end up doing different work tasks. Drawing on a gendered organization perspective and sense of entitlement theory we illustrate the processes whereby structural and cultural expectations place women in predictable and routine work, and men in more developmental work. We also show that the level of education makes a difference to women's sense of entitlement to developmental work, but that the discourse of family friendliness disadvantages women in the allocation of interesting and valued work tasks. The findings illustrate the resilience of gendered work practices and the importance of focusing on workplace interactions to explain this.     

Predicting losses of residential structures in the state of Florida by the public hurricane loss evaluation model

As an environmental phenomenon, hurricanes cause significant property damage and loss of life in coastal areas almost every year. Although a number of commercial loss projection models have been developed to predict the property losses, only a handful of studies are available in the public domain to predict damage for hurricane prone areas. The state of Florida has developed an open, public model for the purpose of probabilistic assessment of risk to insured residential property associated with wind damage from hurricanes. The model comprises three components; viz. the atmospheric science component, the engineering component and the actuarial science component. The atmospheric component includes modeling the track and intensity life cycle of each simulated hurricane within the Florida threat area. Based on historical hurricane statistics, thousands of storms are simulated allowing determination of the wind risk for all residential Zip Code locations in Florida. The wind risk information is then provided to the engineering and actuarial components to model damage and average annual loss, respectively. The actuarial team finds the county-wise loss and the total loss for the entire state of Florida. The computer team then compiles all information from atmospheric science, engineering and actuarial components, processes all hurricane related data and completes the project. The model was submitted to the Florida Commission on Hurricane Loss Projection Methodology for approval and went through a rigorous review and was revised as per the suggestions of the commission. The final model was approved for use by the insurance companies in Florida by the commission. At every stage of the process, statistical procedures were used to model various parameters and validate the model. This paper presents a brief summary of the main components of the model (meteorology, vulnerability and actuarial) and then focuses on the statistical validation of the same.     

Authors’ responses to the discussion on “Predicting losses of residential structures in the state of Florida by the Public Hurricane Loss Evaluation Model”

Hurricanes threaten the Florida coast line every year and are capable of causing catastrophic losses. The Public Hurricane Loss Model was developed in response to the need for having an open transparent model to predict the above losses. The results were summarized in the paper “Predicting Losses of Residential Structures in the State of Florida by the Public Hurricane Loss Model” which was subsequently a subject of discussion by experts in Meteorology, Engineering, Actuarial Sciences and Statistics. The present paper presents a response to the discussions on the above mentioned article.