Research performance evaluation of Chinese university:A non-homogeneous network DEA approach

Performance evaluation for universities or research institutions has become a hot topic in recent years.However,the previous works rarely investigate the multiple departments’performance of a university,and especially,none of them consider the non-homogeneity among the universities’departments.In this paper,we develop data envelopment analysis(DEA)models to evaluate the performance of general non-homogeneous decision making units(DMUs)with two-stage network structures and then apply them to a university in China.Specifically,the first stage is faculty research process,and the second stage is student research process.We first spit each DMU(i.e.department)into a combination of several mutually exclusive maximal input subgroups and output subgroups in terms of their homogeneity in both stages.Then an additive DEA model is proposed to evaluate the performance of the overall efficiency of the non-homogeneous DMUs with two-stage network structure.By analyzing the empirical results,some implications are provided to support the university to promote the research performance of each department as well as the whole university.

Asymptotic Distribution of the Jump Change-Point Estimator

The asymptotic distribution of the change-point estimator in a jump change- point model is considered. For the jump change-point model Xi - α ＋ θ{[nτ0] 〈 i ≤ n} ＋ εi, where εi （i = 1,…. , n） are independent identically distributed random variables with Eεi -= 0 and Var（εi） 〈 ∞, with the help of the slip window method, the asymptotic distribution of the jump change-point estimator τ is studied under the condition of the local alternative hypothesis.