Improved estimation under collinearity and squared error loss

This paper examines the performance of several biased, Stein-like and empirical Bayes estimators for the general linear statistical model under conditions of collinearity. A new criterion for deleting principal components, based on an unbiased estimator of risk, is introduced. Using a squared error measure and Monte Carlo sampling experiments, the resulting estimator's performance is evaluated and compared with other traditional and non-traditional estimators.multicollinearity principal components linear regression Stein rules empirical Bayes estimators unbiased estimation of risk

The myth of sustainability in fashion supply chains

The global fashion market is expected to account for €1,512 billion by 2018. Yet, the fashion industry is associated with critical environmental and social impact due to extensive material use, energy consumption, and safety issues. Therefore, in contrast to traditional supply chain management (SCM), a more sustainable SCM must be introduced by the explicit integration of environmental and social objectives. This study attempts to synthesise both existing and new elements in comprehensive frameworks. The main contribution of this chapter is the application of an assessment tool to evaluate the impact of SC operations on sustainability. Subsequently, a performance measurement model is proposed to assess to what extent the level of sustainability could affect the operational performance areas. An adequate understanding of how SC of a fashion company could be configured toward sustainability, how sustainability must be assessed, and how SSCM performance could be measured is provided through this chapter