Matrix mean squared error comparisons of some biased estimators with two biasing parameters

To deal with multicollinearity problem, the biased estimators with two biasing parameters have recently attracted much research interest. The aim of this article is to compare one of the last proposals given by Yang and Chang (2010 Yang, H., and X. Chang. 2010. A new two-parameter estimator in linear regression. Communications in Statistics: Theory and Methods 39 (6):923–34.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) with Liu-type estimator (Liu 2003 Liu, K. 2003. Using Liu-type estimator to combat collinearity. Communications in Statistics: Theory and Methods 32 (5):1009–20.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) and k ? d class estimator (Sakallioglu and Kaciranlar 2008 Sakallioglu, S., and S. Kaciranlar. 2008. A new biased estimator based on ridge estimation. Statistical Papers 49:669–89.[Crossref], [Web of Science ®] , [Google Scholar]) under the matrix mean squared error criterion. As well as giving these comparisons theoretically, we support the results with the extended simulation studies and real data example, which show the advantages of the proposal given by Yang and Chang (2010 Yang, H., and X. Chang. 2010. A new two-parameter estimator in linear regression. Communications in Statistics: Theory and Methods 39 (6):923–34.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) over the other proposals with increasing multicollinearity level.     

A Comparison of Workers Employed in Hazardous Jobs in Terms of Job Satisfaction,Perceived Job Risk and Stress: Turkish Jean Sandblasting Workers,Dock Workers,Factory Workers and Miners

The purpose of this study is to compare job satisfaction, perception of job risk, stress symptoms and vulnerability to stress of miners, dock workers, jean sandblasting workers and factory workers. A job satisfaction scale and stress audit scale were applied to 220 workers. Results revealed that dock and jean sandblasting workers perceived their work as more risky compared to other workers. Jean sandblasting workers also had the lowest scores on job satisfaction subscales (organisational policies, physical conditions, interpersonal relations, individual factors, autonomy and wage) and total job satisfaction. Likewise, dock workers had lower scores on job satisfaction than factory workers and miners. Analyses also showed that miners did not differ from factory workers in terms of job satisfaction and stress symptoms. Moreover, jean sandblasting workers and dock workers obtained higher scores on stress symptoms. Jean sandblasting workers were also found to be the group that had the highest level of vulnerability to stress. Perceived job risk, vulnerability to stress and stress symptoms were the most important predictors of job satisfaction.     

Reliability estimation in multicomponent stress–strength model for Topp-Leone distribution

In this paper, we consider the estimation reliability in multicomponent stress-strength (MSS) model when both the stress and strengths are drawn from Topp-Leone (TL) distribution. The maximum likelihood (ML) and Bayesian methods are used in the estimation procedure. Bayesian estimates are obtained by using Lindley’s approximation and Gibbs sampling methods, since they cannot be obtained in explicit form in the context of TL. The asymptotic confidence intervals are constructed based on the ML estimators. The Bayesian credible intervals are also constructed using Gibbs sampling. The reliability estimates are compared via an extensive Monte-Carlo simulation study. Finally, a real data set is analysed for illustrative purposes.     

Heteroscedastic and heavy-tailed regression with mixtures of skew Laplace normal distributions

Joint modelling skewness and heterogeneity is challenging in data analysis, particularly in regression analysis which allows a random probability distribution to change flexibly with covariates. This paper, based on a skew Laplace normal (SLN) mixture of location, scale, and skewness, introduces a new regression model which provides a flexible modelling of location, scale and skewness parameters simultaneously. The maximum likelihood (ML) estimators of all parameters of the proposed model via the expectation-maximization (EM) algorithm as well as their asymptotic properties are derived. Numerical analyses via a simulation study and a real data example are used to illustrate the performance of the proposed model.     

Storytelling as a Way to Work Through Intractable Conflicts: The German-Jewish Experience and Its Relevance to the Palestinian-Israeli Context

The storytelling method can be used to work through intractable conflicts. Working-through enables people who have suffered traumatic social experiences to learn to live with these painful events while developing an ability to listen to the pain of the "other." The storytelling approach focuses on the way personal storytelling facilitates the working-through processes in intractable conflicts. The storytelling approach was used in To Reflect and Trust (TRT), a dialogue group that began in 1992 and involved descendants of Nazi perpetrators and Jewish descendants of Holocaust survivors. The storytelling method was applied to a year-long Jewish-Palestinian student workshop held at Ben Gurion University in 2000–2001.     

Robust mixture regression modeling using the least trimmed squares (LTS)-estimation method

Mixture regression models are used to investigate the relationship between variables that come from unknown latent groups and to model heterogenous datasets. In general, the error terms are assumed to be normal in the mixture regression model. However, the estimators under normality assumption are sensitive to the outliers. In this article, we introduce a robust mixture regression procedure based on the LTS-estimation method to combat with the outliers in the data. We give a simulation study and a real data example to illustrate the performance of the proposed estimators over the counterparts in terms of dealing with outliers.     

On the generalization of the hazard rate twisting-based simulation approach

Estimating the probability that a sum of random variables (RVs) exceeds a given threshold is a well-known challenging problem. A naive Monte Carlo simulation is the standard technique for the estimation of this type of probability. However, this approach is computationally expensive, especially when dealing with rare events. An alternative approach is represented by the use of variance reduction techniques, known for their efficiency in requiring less computations for achieving the same accuracy requirement. Most of these methods have thus far been proposed to deal with specific settings under which the RVs belong to particular classes of distributions. In this paper, we propose a generalization of the well-known hazard rate twisting Importance Sampling-based approach that presents the advantage of being logarithmic efficient for arbitrary sums of RVs. The wide scope of applicability of the proposed method is mainly due to our particular way of selecting the twisting parameter. It is worth observing that this interesting feature is rarely satisfied by variance reduction algorithms whose performances were only proven under some restrictive assumptions. It comes along with a good efficiency, illustrated by some selected simulation results comparing the performance of the proposed method with some existing techniques.     

Parameter estimation for mixtures of skew Laplace normal distributions and application in mixture regression modeling

In this article, we propose mixtures of skew Laplace normal (SLN) distributions to model both skewness and heavy-tailedness in the neous data set as an alternative to mixtures of skew Student-t-normal (STN) distributions. We give the expectation–maximization (EM) algorithm to obtain the maximum likelihood (ML) estimators for the parameters of interest. We also analyze the mixture regression model based on the SLN distribution and provide the ML estimators of the parameters using the EM algorithm. The performance of the proposed mixture model is illustrated by a simulation study and two real data examples.     

Interplay of gender subtext and local culture in the organizational logic: the case of a textile factory in Turkey

This paper examines gender as a constitutive element of inequality regimes in the workplace. The organizational logic of workplace is reproduced through accepted but unequal policies and principles. The organizational dynamics of the gendered workplace are embedded in the articulation of global and local markets. The article elaborates on the nature of the organizational logic by focusing on the local dynamics of a Central Anatolian city, Çorum, based on a case study conducted in Turkey. We observe how capitalist organizational logic and gender subtext operate in and through the conservative culture specific to this locality. Through the interactions between employers and employees, the engagement between the capitalist organization and gender subtext constructs the realities of workplace. Workers’ responses to the structural, cultural, interactional and identity dynamics of the organization are to be understood within the triangle of skill, care and honour in capitalist organizational logic. Embedded in all these responses is a specific emphasis on virtuous female worker. Such an ideal worker fits both the capitalist organizational logic and patriarchal system.