Complex Latent Variable Modeling in Educational Assessment

Bayesian item response theory models have been widely used in different research fields. They support measuring constructs and modeling relationships between constructs, while accounting for complex test situations (e.g., complex sampling designs, missing data, heterogenous population). Advantages of this flexible modeling framework together with powerful simulation-based estimation techniques are discussed. Furthermore, it is shown how the Bayes factor can be used to test relevant hypotheses in assessment using the College Basic Academic Subjects Examination (CBASE) data.     

Local estimation for varying-coefficient models with longitudinal data

Varying-coefficient models are very useful for longitudinal data analysis. In this paper, we focus on varying-coefficient models for longitudinal data. We develop a new estimation procedure using Cholesky decomposition and profile least squares techniques. Asymptotic normality for the proposed estimators of varying-coefficient functions has been established. Monte Carlo simulation studies show excellent finite-sample performance. We illustrate our methods with a real data example.     

Green supply chain management: the case of the construction sector in the United Arab Emirates (UAE)

Restraining the negative environmental impacts of the construction sector constitutes one of the major challenges of the twenty-first century. However, efforts to address it have been largely fragmented. With environmental consequences of a construction project typically dispersed across its life cycle, i.e. from design through to end-of-life, greening this sector requires a supply chain wide focus inclusive of all key stages and stakeholders; also, all relevant aspects such as the nature of green practices implemented and associated drivers, barriers and performance implications need to be considered. This forms the focus of the present study where a comprehensive, green supply chain management oriented understanding of the construction sector is developed through the context of the UAE construction sector, and incorporating inputs from all key stakeholders, i.e. Developers, Architects/Consultants, Contractors and (material) Suppliers. The study contributes to improving the efficiency and effectiveness of greening of the construction sector.     

Attributing Outcomes to Social Policy Interventions – ‘Gold Standard’ or ‘Fool's Gold’ in Public Policy and Management?

In recent years there has been increased interest in outcome‐based social policy‐making and management. The UK has been in the forefront of this movement but similar movements have been identified internationally. This interest in outcome‐based decision‐making has been given particular impetus through the ‘results’‐based movement in evaluation and performance management since the 1980s, which has increased in scope over time, slowly changing its emphasis from cost reduction and measuring outputs to measuring outcomes. This change has been widely welcomed by policymakers, practitioners and academics. However, there is evidence that the reality is often rather less than the rhetoric. Moreover, the ‘attribution problem’ of attributing changes in outcomes to specific social policies has remained a major issue. The conceptual solution of constructing ‘cause‐and‐effect’ models, imported from the policy evaluation field, has only recently become common for operationalising these models. This article outlines the evolution of interest in outcome‐based social policy‐making up to recent times and the growing realization of the importance of the attribution problem. It then outlines both how the ‘cause‐and‐effect’ policy modelling approach can partially tackle the attribution problem, but also its inherent limitations. Lastly, the article uses several case studies in current UK social policy‐making to demonstrate the potential importance of the reasoning embedded within cause‐and‐effect models but also the dangers in policy‐making which adopts this approach without understanding its conceptual basis or in fields where it is inappropriate, given the current state of our knowledge of social policy systems.     

Statistical estimation of internal available bandwidth

This paper presents a method for using end-to-end available bandwidth measurements in order to estimate available bandwidth on individual internal links. The basic approach is to use a power transform on the observed end-to-end measurements, model the result as a mixture of spatially correlated exponential random variables, carryout estimation by moment methods, then transform back to the original variables to get estimates and confidence intervals for the expected available bandwidth on each link. Because spatial dependence leads to certain parameter confounding, only upper bounds can be found reliably. Simulations with ns2 show that the method can work well and that the assumptions are approximately valid in the examples.     

Assessing the performance of variational methods for mixed logistic regression models

We present a variational estimation method for the mixed logistic regression model. The method is based on a lower bound approximation of the logistic function [Jaakkola, J.S. and Jordan, M.I., 2000, Bayesian parameter estimation via variational methods. Statistics & Computing, 10, 25–37.]. Based on the approximation, an EM algorithm can be derived that results in a considerable simplification of the maximization problem in that it does not require the numerical evaluation of integrals over the random effects. We assess the performance of the variational method for the mixed logistic regression model in a simulation study and an empirical data example, and compare it to Laplace's method. The results indicate that the variational method is a viable choice for estimating the fixed effects of the mixed logistic regression model under the condition that the number of outcomes within each cluster is sufficiently high.     

Adaptation of the jackknifed ridge methods to the linear mixed models

The purpose of this article is to obtain the jackknifed ridge predictors in the linear mixed models and to examine the superiorities, the linear combinations of the jackknifed ridge predictors over the ridge, principal components regression, r?k class and Henderson's predictors in terms of bias, covariance matrix and mean square error criteria. Numerical analyses are considered to illustrate the findings and a simulation study is conducted to see the performance of the jackknifed ridge predictors.     

Robust bootstrap estimates in heteroscedastic semi-varying coefficient models and applications in analyzing Australia CPI data

This article deals with the estimation of the parametric component, which is of primary interest, in the heteroscedastic semi-varying coefficient models. Based on the bootstrap technique, we present a procedure for estimating the parameters, which can provide a reliable approximation to the asymptotic distribution of the profile least-square (PLS) estimator. Furthermore, a bootstrap-type estimator of covariance matrix is developed, which is proved to be a consistent estimator of the covariance matrix. Moreover, some simulation experiments are conducted to evaluate the finite sample performance for the proposed methodology. Finally, the Australia CPI dataset is analyzed to demonstrate the application of the methods.     

Global adjustment to US disengagement from the world trading system

US trade policy in the Trump administration is protectionist, raising tariffs outside of WTO rules, threatening trade wars, withdrawing from existing trade agreements, and negotiating new bilateral trade deals rather than free trade agreements. The reaction of the rest of the world has been to support the WTO rules-based trading system and to pursue new and expanded regional trade agreements that do not include the US. The result may be US disengagement from the world trading system, with countries diverting trade around the US. We use a global computable general equilibrium (CGE) model to analyze changes in trade and production patterns for a variety of scenarios, including US trade wars. We find that adjustment to US disengagement is feasible—the changes in trade shares as countries divert trade are modest and achievable, with very little impact on the volume of global trade. If the US engages in trade wars with major partners, the best response for other trade partners is to sit out the trade war, accept higher US tariffs, and gain markets from global trade diversion around the US. Finally, because of indirect effects, widespread tariffs fail to benefit the protected sectors—a fallacy of composition in a protectionist trade policy.