从和谐共存看环境犯罪的预防对策

当前,随着全球经济的快速发展,环境问题日益严重,环境犯罪随之增多,这一问题在我国尤为突出,这与构建和谐社会,实现人与自然和谐发展的主题相悖离,在新的社会形势下,必须探寻积极有效的预防对策,抑制和打击环境犯罪,实现和谐共存的人类社会的共同理想和发展目标.     

网络内容的技术控制模式建构与评析

基于维纳的经典控制模式,依照接入控制、过滤控制、编辑控制、分析控制四个流程,尝试建构了网络内容的技术控制模式。此模式着眼于复杂的网络传播环境,反映了多传播时代的多向反馈,体现了效果/批判的范式融合,综合了立体多元的控制手段。     

参政党利益表达机制建设的路径探析

利益表达是政党的基本功能,构建社会主义和谐社会需要参政党发挥利益表达功能。但在实际政治过程中参政党的这一功能并没有得到充分展现。为构建社会主义和谐社会,必须从多方面着手加强参政党利益表达机制建设。     

中国社会发展水平综合评价研究

一、问题的提出自20世纪90年代以来,人类发展问题已成为当代社会人们研究的重大课题。诚然,人类社会是发展了,但发展的现实世界中还有许多不尽如人意之处。如经济发展了,却带来许多产品生产过剩的问题;人口平均寿命提高了,却又伴随着出现老龄人口的社会保障问题;经济发展的速度     

中美石油安全水平对经济增长影响的对比研究——基于灰色关联分析

为科学地评价比较中美石油安全水平对经济增长的影响程度,文章采用主成分分析方法,选取采储比水平、储量接替率、消费弹性系数、原油价格波动系数、对外依存度和石油占一次能源消费比重等6个要素指标,构成一个新的石油安全指标评价体系,并利用灰色关联方法比较分析了不同时间段内中美两国的石油安全水平与经济增长之间的关联度,得出自21世纪以来,美国经济发展对石油安全水平的依赖程度要远大于中国的结论。     

Complete convergence for arrays of rowwise negatively superadditive-dependent random variables and its applications

In this paper, the Rosenthal-type maximal inequalities and Kolmogorov-type exponential inequality for negatively superadditive-dependent (NSD) random variables are presented. By using these inequalities, we study the complete convergence for arrays of rowwise NSD random variables. As applications, the Baum–Katz-type result for arrays of rowwise NSD random variables and the complete consistency for the estimator of nonparametric regression model based on NSD errors are obtained. Our results extend and improve the corresponding ones of Chen et al. [On complete convergence for arrays of rowwise negatively associated random variables. Theory Probab Appl. 2007;52(2):393–397] for arrays of rowwise negatively associated random variables to the case of arrays of rowwise NSD random variables.     

Regression Analysis of Left-truncated and Case I Interval-censored Data with the Additive Hazards Model

In recent years the analysis of interval-censored failure time data has attracted a great deal of attention and such data arise in many fields including demographical studies, economic and financial studies, epidemiological studies, social sciences, and tumorigenicity experiments. This is especially the case in medical studies such as clinical trials. In this article, we discuss regression analysis of one type of such data, Case I interval-censored data, in the presence of left-truncation. For the problem, the additive hazards model is employed and the maximum likelihood method is applied for estimations of unknown parameters. In particular, we adopt the sieve estimation approach that approximates the baseline cumulative hazard function by linear functions. The resulting estimates of regression parameters are shown to be consistent and efficient and have an asymptotic normal distribution. An illustrative example is provided.     

Establishing decision tree-based short-term default credit risk assessment models

ABSTRACT

Traditional credit risk assessment models do not consider the time factor; they only think of whether a customer will default, but not the when to default. The result cannot provide a manager to make the profit-maximum decision. Actually, even if a customer defaults, the financial institution still can gain profit in some conditions. Nowadays, most research applied the Cox proportional hazards model into their credit scoring models, predicting the time when a customer is most likely to default, to solve the credit risk assessment problem. However, in order to fully utilize the fully dynamic capability of the Cox proportional hazards model, time-varying macroeconomic variables are required which involve more advanced data collection. Since short-term default cases are the ones that bring a great loss for a financial institution, instead of predicting when a loan will default, a loan manager is more interested in identifying those applications which may default within a short period of time when approving loan applications. This paper proposes a decision tree-based short-term default credit risk assessment model to assess the credit risk. The goal is to use the decision tree to filter the short-term default to produce a highly accurate model that could distinguish default lending. This paper integrates bootstrap aggregating (Bagging) with a synthetic minority over-sampling technique (SMOTE) into the credit risk model to improve the decision tree stability and its performance on unbalanced data. Finally, a real case of small and medium enterprise loan data that has been drawn from a local financial institution located in Taiwan is presented to further illustrate the proposed approach. After comparing the result that was obtained from the proposed approach with the logistic regression and Cox proportional hazards models, it was found that the classifying recall rate and precision rate of the proposed model was obviously superior to the logistic regression and Cox proportional hazards models.     

Model Averaging for Prediction With Fragmentary Data

ABSTRACT

One main challenge for statistical prediction with data from multiple sources is that not all the associated covariate data are available for many sampled subjects. Consequently, we need new statistical methodology to handle this type of “fragmentary data” that has become more and more popular in recent years. In this article, we propose a novel method based on the frequentist model averaging that fits some candidate models using all available covariate data. The weights in model averaging are selected by delete-one cross-validation based on the data from complete cases. The optimality of the selected weights is rigorously proved under some conditions. The finite sample performance of the proposed method is confirmed by simulation studies. An example for personal income prediction based on real data from a leading e-community of wealth management in China is also presented for illustration.