Inverse Lindley power series distributions: a new compounding family and regression model with censored data

This paper introduces a new class of distributions by compounding the inverse Lindley distribution and power series distributions which is called compound inverse Lindley power series (CILPS) distributions. An important feature of this distribution is that the lifetime of the component associated with a particular risk is not observable, rather only the minimum lifetime value among all risks is observable. Further, these distributions exhibit an unimodal failure rate. Various properties of the distribution are derived. Besides, two special models of the new family are investigated. The model parameters of the two sub-models of the new family are obtained by the methods of maximum likelihood, least square, weighted least square and maximum product of spacing and compared them using the Monte Carlo simulation study. Besides, the log compound inverse Lindley regression model for censored data is proposed. Three real data sets are analyzed to illustrate the flexibility and importance of the proposed models.     

Performance of asymmetric links and correction methods for imbalanced data in binary regression

In binary regression, imbalanced data result from the presence of values equal to zero (or one) in a proportion that is significantly greater than the corresponding real values of one (or zero). In this work, we evaluate two methods developed to deal with imbalanced data and compare them to the use of asymmetric links. The results based on simulation study show, that correction methods do not adequately correct bias in the estimation of regression coefficients and that the models with power links and reverse power considered produce better results for certain types of imbalanced data. Additionally, we present an application for imbalanced data, identifying the best model among the various ones proposed. The parameters are estimated using a Bayesian approach, considering the Hamiltonian Monte-Carlo method, utilizing the No-U-Turn Sampler algorithm and the comparisons of models were developed using different criteria for model comparison, predictive evaluation and quantile residuals.     

Green supply chain performance measurement using the analytic hierarchy process: a comparative analysis of manufacturing organisations

The main purpose of this research is to develop and deploy an analytical framework for measuring the environmental performance of manufacturing supply chains. This work's theoretical bases combine and reconcile three major areas: supply chain management, environmental management and performance measurement. Researchers have suggested many empirical criteria for green supply chain (GSC) performance measurement and proposed both qualitative and quantitative frameworks. However, these are mainly operational in nature and specific to the focal company. This research develops an innovative GSC performance measurement framework by integrating supply chain processes (supplier relationship management, internal supply chain management and customer relationship management) with organisational decision levels (both strategic and operational). Environmental planning, environmental auditing, management commitment, environmental performance, economic performance and operational performance are the key level constructs. The proposed framework is then applied to three selected manufacturing organisations in the UK. Their GSC performance is measured and benchmarked by using the analytic hierarchy process (AHP), a multiple-attribute decision-making technique. The AHP-based framework offers an effective way to measure and benchmark organisations’ GSC performance. This study has both theoretical and practical implications. Theoretically it contributes holistic constructs for designing a GSC and managing it for sustainability; and practically it helps industry practitioners to measure and improve the environmental performance of their supply chain.     

Supply chain integration framework using literature review

Definitions and measures of supply chain integration (SCI) are diverse. More empirical research, with clear definition and appropriate measures are needed. The purpose of this article is to identify dimensions and variables for SCI and develop an integrated framework to facilitate this. A literature review of the relevant academic papers in international journals in Logistics, Supply Chain Management and Operations Management for the period 1995–2009 has been undertaken. This study reveals that information integration, coordination and resource sharing and organisational relationship linkage are three major dimensions for SCI. The proposed framework helps integrate both upstream suppliers and downstream customers with the focal organisation. It also allows measuring SCI using both qualitative and quantitative approach. This study encourages researchers and practitioners to identify dimensions and variables for SCI and analyses how it affects the overall supply chain (SC) performance in terms of efficiency and responsiveness. Although there is extensive research in the area of SCI, a comprehensive and integrated approach is missing. This study bridges the gap by developing a framework for measuring SCI, which enables any organisation to identify critical success factors for integrating their SC, measures the degree of integration qualitatively and quantitatively and suggest improvement measures.     

Project risk management using multiple criteria decision-making technique and decision tree analysis: a case study of Indian oil refinery

This study proposes an integrated analytical framework for effective management of project risks using combined multiple criteria decision-making technique and decision tree analysis. First, a conceptual risk management model was developed through thorough literature review. The model was then applied through action research on a petroleum oil refinery construction project in the Central part of India in order to demonstrate its effectiveness. Oil refinery construction projects are risky because of technical complexity, resource unavailability, involvement of many stakeholders and strict environmental requirements. Although project risk management has been researched extensively, practical and easily adoptable framework is missing. In the proposed framework, risks are identified using cause and effect diagram, analysed using the analytic hierarchy process and responses are developed using the risk map. Additionally, decision tree analysis allows modelling various options for risk response development and optimises selection of risk mitigating strategy. The proposed risk management framework could be easily adopted and applied in any project and integrated with other project management knowledge areas.     

Volunteerism Among Social Work Students During Hurricanes Katrina and Rita

Abstract

This study examined volunteer activities and predictors of volunteerism among graduate and undergraduate social work students (N = 416) from four universities in the Gulf Coast area following hurricanes Katrina and Rita. The convenience sample drawn from all social work students at these universities included 89% females, and 52% of the students were in a master's level program. Sixty-one percent of the respondents were African American and 31% were non-Hispanic white. The majority of students volunteered (97%) despite experiencing a range of hurricane-related stressors. Multivariate analyses yielded a regression model that best predicted heightened volunteerism, which included age, school site, previous volunteer experience, hurricane-related stressors, altruism, and increased commitment to social work values (R ² = .35). The strongest predictors of volunteerism were stressor, increased commitment to social work values, and altruism (betas = .30, .26, and .21 respectively). The findings are consistent with previous research on volunteering in times of disaster and with current thinking about posttraumatic growth .     

Critical reflective thinking through service‐learning in multicultural teacher education

This paper describes and discusses the effect of service‐learning on students’ reflective thinking about their own knowledge in multicultural teacher education at a state university in Southern California (USA). Two versions of students’ multicultural autobiographies, one at the beginning and the other at the end of the course, were examined to determine their individual progress. Two classes, one with a service‐learning assignment and the other without, were involved to identify differences. Using King and Kitchener’s model of reflective judgment, this study compares and contrasts the progress of student critical reflective thinking and differences between the two classes. The research suggests that service‐learning stimulated and increased students’ reflective thinking about their own knowledge in multicultural education.     

On estimating the reliability in a multicomponent stress-strength model based on Chen distribution

Abstract

In this article, we obtain point and interval estimates of multicomponent stress-strength reliability model of an s-out-of-j system using classical and Bayesian approaches by assuming both stress and strength variables follow a Chen distribution with a common shape parameter which may be known or unknown. The uniformly minimum variance unbiased estimator of reliability is obtained analytically when the common parameter is known. The behavior of proposed reliability estimates is studied using the estimated risks through Monte Carlo simulations and comments are obtained. Finally, a data set is analyzed for illustrative purposes.