Search

In public-private partnerships (PPPs), escalation of commitment (EOC) of investors often occurs when receiving negative feedback, leading to a great waste of resources and not conducive to the sustainable development of PPPs. The degree of project completion and decision-making responsibilities of investors with different conscientiousness and neuroticism may affect subsequent resource allocation and further influence their escalation behaviour. Thus, through scenario simulation, this paper constructs path analysis to illustrate the formation mechanism of EOC by investors with diverse conscientiousness and neuroticism in different degrees of decision-making responsibilities and project completion, in which confidence in completing the projects may present a mediating effect. The empirical results show that completion degree and decision-making responsibilities both positively affect investors’ EOC and that the interaction is significant. The impact mechanism of conscientiousness and neuroticism on EOC varies in different project scenarios. Then, some targeted recommendations are proposed to curb EOC. The findings provide scientific evidence for governments to conduct effective governance of EOC in PPPs.

In the process of measuring the power consumed in buildings,massive quantity of real-time energy consumption data have been accumulated.Salient features of these data include large samples,noise accumulations and the presence of measurement errors,etc.Thus,how to analyze and apply these massive data becomes a very challengeable problem.In this paper,based on the dataset which include the consumption of large-scale public buildings in Shanghai for 2015,we establish a hierarchical Bayesian model to estimate the average monthly consumption and the average annual consumption of large public-scale buildings in 2015.The results will help government regulators to conduct effective evaluation on energy saving for buildings.

The aim of this paper is to explore variable selection approaches in the partially linear proportional hazards model for multivariate failure time data. A new penalised pseudo-partial likelihood me...

In this paper, we propose a composite minimizing average check loss estimation(CMACLE)method for the composite quantile regression(CQR)of the partial linear single-index model(PLSIM)by local linear method. Based on constructive approach, the estimators by CMACLE are able to achieve the best convergence rate. The asymptotical normalities of the estimators are also derived. Meanwhile, the asymptotic efficiency of the CQR estimation relative to the mean regression are investigated. Further more, we propose a variable selection method for the CQR of PLSIM by combining the CMACLE procedure with the adaptive LASSO penalized method. The oracle properties of the proposed variable selection method are also established. Simulations with various non-normal errors and a real data analysis are conducted to assess the finite sample property of the proposed estimation and variable selection methods.

The neutron resonance parameters as well as the thermal neutron cross sections of more than fifty nuclides have been evaluated for CENDL. From the recommended resolved resonance parameters, the average resonance parameters have been estimated and recommended. Some characteristics of the average parameters have been studied. Efforts are being made to compare the cross sections calculated on the basis of the evaluated parameters with the measured ones and to obtain the residual cross sections.