马克思社会形态理论内在逻辑的当代解读

马克思社会形态理论是唯物史观的基础性组成部分和核心思想,它所着重说明的是人类社会的进步是一个社会形态更替和发展的有规律的历史过程.这一理论包含着对人类社会的横向说明和纵向阐释两个方面内容.当下对前者的把握需要突出地将作为历史活动主体的人的实践活动与社会形态的变化联系在一起考察;对于后者的把握需要突出地将马克思社会形态演进与资本主义社会的暂时性和历史性联系在一起考察.20世纪以来社会主义和资本主义的历史,都是马克思社会形态理论科学性的具体说明.     

科研活动的信息不对称与科研管理的作用：问题和对策

科学研究是典型的智力密集型劳动,因为个人天赋和脑力劳动的不可观测性,信息不对称成为科研活动的一个重要特征,引发众多的逆向选择和道德风险问题,降低了科研市场的总体运行效率。预防和减少科研市场中的逆向选择和道德风险,提高科研市场的运行效率,是信息不对称条件下科研管理的重要作用。在当前的科研体制下,应当重新认识科研管理的作用,正确定位科研管理职能,确保科研管理部门的相对独立性,完善针对科研人员和科研管理部门的激励机制,建立互联共享的科研信息数据库,才能有效减少逆向选择和道德风险造成的科研市场效率损失。     

Confidence intervals for finite difference solutions

Although applications of Bayesian analysis for numerical quadrature problems have been considered before, it is only very recently that statisticians have focused on the connections between statistics and numerical analysis of differential equations. In line with this very recent trend, we show how certain commonly used finite difference schemes for numerical solutions of ordinary and partial differential equations can be considered in a regression setting. Focusing on this regression framework, we apply a simple Bayesian strategy to obtain confidence intervals for the finite difference solutions. We apply this framework on several examples to show how the confidence intervals are related to truncation error and illustrate the utility of the confidence intervals for the examples considered.     

Generalized anticipated backward stochastic differential equations driven by Brownian motion and continuous increasing process*

This article mainly considers a new class of anticipated BSDEs driven by Brownian motion and continuous increasing process, which are called generalized anticipated backward stochastic differential equations(GABSDEs). We first give the form of GABSDEs. Then, existence and uniqueness result for GABSDEs is established as well as a comparison theorem is obtained under the certain assumptions. At last, we give an application about the duality between SDDEs and GABSDEs.     

Quickly variable selection for varying coefficient models with missing response at random

This paper focuses on the variable selections for a varying coefficient models with missing response at random. A procedure is presented by basis function approximations with smooth-threshold estimating equations. Furthermore, the proposed method selects significant variables and estimates coefficients simultaneously avoiding the problem of solving a convex optimization, which reduced the burden of computation. Compared to existing equation based approaches, our procedure is more efficient and quick. With proper choices the regularization parameter, the resulting estimates perform an oracle property. A cross-validation for tuning parameter selection is also proposed, a numerical study confirms the performance of the proposed method.     

Performance of the stein-type two-parameter estimator in multiple linear regression model

In this article, we consider the Stein-type approach to the estimation of the regression parameter in a multiple regression model under a multicollinearity situation. The Stein-type two-parameter estimator is proposed when it is suspected that the regression parameter may be restricted to a subspace. The bias and the quadratic risk of the proposed estimator are derived and compared with the two-parameter estimator (TPE), the restricted TPE and the preliminary test TPE. The conditions of superiority of the proposed estimator are obtained. Finally, a real data example is provided to illustrate some of the theoretical results.     

Comparisons of several Pareto distributions based on record values

In order to avoid wrong conclusions in any further analysis, it is of importance to conduct a formal comparison for characteristic quantities of the distributions. These characteristic quantities we are familiar with include mean, quantity and reliability function, and so on. In this paper, we consider two tests aiming at the comparisons for function of parameters in Pareto distribution based on record values. They are generalized p-value-based test and parametric bootstrap-based test, respectively. The resulting procedures are easy to compute and are applicable to small samples. A simulation study is conducted to investigate and compare the performance of the proposed tests. A phenomenon we note is that generalized p-value-based test almost uniformly outperforms the parametric bootstrap-based test.     

Performance of the difference-based Liu-type estimator in partially linear model

This paper discusses the parameter estimation in a partially linear model. We proposed a difference-based Liu-type estimator of the unknown parameters in the partially linear model. The asymptotically properties of the proposed estimator are discussed. We propose a iterative method to choose the biasing parameters. Finally, a simulation study and a numerical example are presented to explain the performance of the estimators.