道德原则与道德重构

社会主义市场经济条件下的道德原则应包括三方面内容 :尊重和发展个性的原则 ,公利的原则和和谐的原则。在此基础上 ,建构出具有现代意义的高度发达高度文明的新的经济和价值观念模式 ,正是当代哲学伦理学存在之意义所在     

文姜小议

春秋时齐国女子文姜因性生活问题,遭到后人多方面的贬斥.文姜出嫁鲁国后,鲁国在齐鲁争强中逐渐失势,文姜数度以国君之礼与齐国国君会晤,协调齐鲁关系,增强了鲁国的实力和地位,但史家们却未给予应有的评价.本文认为,文姜不失为古代一位颇有才能的女外交家,不应以瑕掩瑜.     

职业生涯研究

一职业构成状况是社会学研究中重要的变量之一。从宏观的角度看,一个社会的职业构成表明了该社会的分工水平;从微观角度看,社会行动者从事的某种职业说明着个人的社会地位。这两方面多在传统社会学研究中有所体现,其研究更多地具有结构性特征。职业生涯概念的引入力图对此有所突破,一方面它试图把宏观层面和微观层面的职业研究相结合,既关注社会行动者在劳动分工中占有的一系列职业地位,也关注社会行动者职业变动背后的社会因素;另一方面,它也试图把过程因素与结构因素相结合,个人的职业发展或职业连续性的变动在职业生涯的概念下成为社会的…     

天津南港工业区综合服务区景观构建

一、项目背景 天津市南港工业区位于环渤海经济带中部天津滨海新区的大港区,是通过围海造田而兴建的新的大型工业区。综合服务区位于工业区西北部,隶属于工业区一期规划范围,包括南港开发指挥部、工业展览中心、各种行政办公区域、银行、邮局、超市等办公和服务性区域,总体规划面积15万平方米。由于南港工业区土地主要以围海造田为主,场地土壤盐渍化较重。     

On a statistic useful in dimensionality reduction in multivariable linear stochastic system

In this paper we study the sampling properties of a test statistic which has important applications in the area of linear stochastic control systems with multi-inputs and multi-outputs. The statistic is the ratio of a partial sum of the eigenvalues of a sample covariance matrix and its trace. It turns out that using a method due to Sugiura we may derive a useful approximation for its distribution up to and including terms of order l/n, where n denotes the appropriate size. Numerical illustrations using real data are given.     

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.     

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.     

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.     

Semiparametric partially linear varying coefficient models with panel count data

This paper studies semiparametric regression analysis of panel count data, which arise naturally when recurrent events are considered. Such data frequently occur in medical follow-up studies and reliability experiments, for example. To explore the nonlinear interactions between covariates, we propose a class of partially linear models with possibly varying coefficients for the mean function of the counting processes with panel count data. The functional coefficients are estimated by B-spline function approximations. The estimation procedures are based on maximum pseudo-likelihood and likelihood approaches and they are easy to implement. The asymptotic properties of the resulting estimators are established, and their finite-sample performance is assessed by Monte Carlo simulation studies. We also demonstrate the value of the proposed method by the analysis of a cancer data set, where the new modeling approach provides more comprehensive information than the usual proportional mean model.