Two-Group Classification Using Neural Networks*

Artificial neural networks are new methods for classification. We investigate two important issues in building neural network models; network architecture and size of training samples. Experiments were designed and carried out on two-group classification problems to find answers to these model building questions. The first experiment deals with selection of architecture and sample size for different classification problems. Results show that choice of architecture and choice of sample size depend on the objective: to maximize the classification rate of training samples, or to maximize the generalizability of neural networks. The second experiment compares neural network models with classical models such as linear discriminant analysis and quadratic discriminant analysis, and nonparametric methods such as k-nearest-neighbor and linear programming. Results show that neural networks are comparable to, if not better than, these other methods in terms of classification rates in the training samples but not in the test samples.     

The Contingent-Claims Approach to Investment Decisions*

This paper presents a contingent-claims approach to project valuation when capital expenditures are made sequentially over time. It focuses on an important facet of sequential investment projects that the firm can undertake—or pass up projects—as more information becomes available. The contingent-claims approach takes account of this important feature of firms' investment decision process, whereas the traditional capital budgeting procedure does not. Since the traditional method does not reflect the options nature of investment opportunities, it underestimates the value of sequential investment projects. As a result, a naive implementation of the traditional capital budgeting procedure could result in rejecting profitable projects. The extent of undervaluation associated with the traditional capital budgeting procedure is greater when the correlation between the random component of the future asset value and that of the required capital expenditure is smaller and/or when the growth rate of the required capital expenditure is higher.     

丹麦劳动力市场模式分析

丹麦劳动力市场模式是近年来国际劳动力市场模式中非常成功的一种模式,日益受到国际社会的关注。“丹麦模式”的基本特征是将灵活化的劳动力市场、慷慨的福利制度与积极的劳动力市场政策有机结合在一起构成了一个“金三角”,成功地平衡了劳动力市场的灵活性与安全性。丹麦劳动力市场模式以其传统的价值理念、良好的社会合作机制和混合经济体制为基础。随着全球化时代社会经济条件的变化,丹麦劳动力市场模式也面临着严峻的挑战。     

对现代苏南模式转型发展的思考

"十二五"时期,是江苏全面建成小康社会并向率先基本实现现代化迈进的关键时期,苏南地区更是要走在江苏的前列,争取早日实现现代化。苏南经济社会的又好又快发展与在实践中走出来的苏南模式是息息相关的。在国内外经济形势变化的情况下,现代苏南模式要继承苏南模式的核心价值,把握好转型发展、创新发展和开放发展的内涵,积极发展民营经济,加快发展传统文化产业,增强模式的内生动力,助力江苏早日实现现代化。     

A Comment on Second-Order Polynomial Regression Models

In a recent article, Chatterjee and Greenwood [1] addressed the problem of multicollinearity in polynomial regression models. They noted that there is a high correlation between X and X²; therefore, a second-order polynomial model suffers the consequences of collinearity. Chatterjee and Greenwood [1] suggested a method they believe will overcome the problem. The contention of the present comment is that the suggested method accomplishes nothing and, indeed, has the potential to lead the unwary researcher to the wrong inference and misinterpretation of his results.     

A Decision Model for the Robot Selection Problem Using Robust Regression*

Industrial robots are increasingly used by many manufacturing firms. The number of robot manufacturers has also increased with many of these firms now offering a wide range of models. A potential user is thus faced with many options in both performance and cost. This paper proposes a decision model for the robot selection problem. The proposed model uses robust regression to identify, based on manufacturers' specifications, the robots that are the better performers for a given cost. Robust regression is used because it identifies and is resistant to the effects of outlying observations, key components in the proposed model. The robots selected by the model become candidates for testing to verify manufacturers' specifications. The model is tested on a real data set and an example is presented.     

On Sampling Error in Market Share Estimates*

The relative error in the usual estimator of a brand's market share is reformulated in terms of marketing parameters. Such error is shown to be influenced in an important way by market penetration, as well as by variation in brand and product category volume. Of particular interest is the result that the relative error does not depend on the actual share level. Using data from a marketing research firm that supplies share estimates to the health products industry, we find that the relative error may be substantial even when a large sample is available. An upper bound on this relative error is obtained using marketing parameters that can frequently be measured using industry data and a company's internal records, thus reducing the level of judgmental input required in the planning of sample surveys.     

西方儿童虐待成因的发展生态模型理论研究述评

发展生态模型是西方儿童虐待成因研究的重要理论框架,但国内对此缺少系统性介绍。该理论认为,儿童虐待产生自互动个体与嵌套生态系统层面间的交互影响,它主要以生态系统理论为理论基础,并以“生态整合模型”和“生态交互模型”为代表性理论模型。该理论具有重要的方法论意义,它可促使研究者突破单因素变量研究的窠臼,并转向多因素共构视角去理解儿童虐待成因;同时,也为儿童虐待治理实践提供了多因素和多层次的系统干预视角。该理论的出现丰富了儿童虐待研究者的理论想象力,可为我国家庭、学校等场域的儿童虐待和儿童保护研究和干预提供启示。     

城镇企业职工基本养老保险基金支付能力预测研究

随着人口老龄化进程的加剧。我国城镇企业职工基本养老保险基金收支出现了巨大缺口并呈现逐步扩大的趋势,导致基金支付能力持续下降。本文应用社会保障精算方法与技术,以社会养老保险现行政策为导向。引入2005年颁布的《国务院关于完善企业职工基本养老保险制度的决定》中新养老金计发办法的经济参数。构建城镇企业职工基本养老保险基金收入与基金支出测算模型,其中以2006年新政策实施年为时间界限。分别构建“老人”、2006年前退休的“中人”（“老中人”）、2006年后退休的“中人”（“新中人”）、“新人”四类群体的基本养老保险基金支出洲算模型。从而推导出城镇企业职工基本养老保险基金支付能力预测精算模型。以期对养老保险基金失衡风险和支竹能力危机进行及时预测。并为社会养老保险政策调整和制度完善提供量化依据。