Multiple aspect testing for case-control designs

When testing for the equality of two distributions in a case-control design with treatment effects presumed to act possibly on more than one aspect, different tests may be properly considered for testing for different features of a null hypothesis, leading to the multiple aspect testing issue. Two different aspects are therefore of interest: the location-aspect, based on the comparison of location indexes, and the distributional-aspect, based on the comparison of the empirical distribution functions. A simulation study shows that the combined testing procedure exhibits a good robust overall performance, and an application in biomedical research is also presented.     

基于主成分分析的多元线性回归用水量模型研究——以安徽省为例

用水量的准确预测对水资源规划具有重要意义。根据统计资料,选取了八项主要影响用水量变化的指标。按照主成分分析法原理,运用少数综合因子对影响用水量变化的原有八项指标进行了综合和简化,并研究其贡献率及综合效应;分析了用水量增长的特点,选用Logistic方程对综合因子进行回归分析;结合多元线性回归方法,建立了安徽省用水量模型;拟合结果表明,模型具有较高的精度;运用该模型,对安徽省2010年的用水量进行了预测;为水资源规划与管理提供参考依据。     

ML– and semiparametric estimation in logistic models with incomplete covariate data

ML–estimation of regression parameters with incomplete covariate information usually requires a distributional assumption regarding the concerned covariates that implies a source of misspecification. Semiparametric procedures avoid such assumptions at the expense of efficiency. In this paper a simulation study with small sample size is carried out to get an idea of the performance of the ML–estimator under misspecification and to compare it with the semiparametric procedures when the former is based on a correct assumption. The results show that there is only a little gain by correct parametric assumptions, which does not justify the possibly large bias when the assumptions are not met. Additionally, a simple modification of the complete case estimator appears to be nearly semiparametric efficient.     

An effective going concern prediction model for the sustainability of enterprises and capital market development

The purpose of this study is to construct a two-stage effective and innovative going concern prediction model to predict going concern doubt for the sustainability of enterprises and capital market development. Samples of this study are the companies listed on the Taiwan Stock Exchange or the Taipei Exchange, totalling 196 companies and including 49 companies with going concern doubt and 147 normal companies (with no going concern doubt). The data are taken from the Taiwan Economic Journal (TEJ) and the Market Observation Post System during the period from 2001 to 2016 (totalling 16 years). This study adopts a two-stage way to construct the going concern prediction models. In Stage I, the traditional statistical method of stepwise regression (SR) and the data mining technique artificial neural network (ANN) are applied to select the important variables. In Stage II, two decision tree algorithms (data mining techniques): classification and regression tree (CART) and C5.0 are used to establish the prediction models. The results show that the SR + CART model has the highest going concern prediction accuracy, with an overall accuracy of 87.42%.     

Bayesian assessment of dimensionality in reduced rank regression

We consider Bayesian inference about the dimensionality in the multivariate reduced rank regression framework, which encompasses several models such as MANOVA, factor analysis and cointegration models for multiple time series. The fractional Bayes approach is used to derive a closed form approximation to the posterior distribution of the dimensionality and some asymptotic properties of the approximation are proved. Finite sample properties are studied by simulation and the method is applied to growth curve data and cointegrated multivariate time series.     

中国电子商务企业生存的实证分析

本文通过Logisitc回归模型，对电子商务企业生存的影响因素进行了分析。笔者认为，电子商务企业的生存是受企业自身基因构成决定的，而电子商务企业的基因构成包括了企业家才能、资金、信息资本、人力资本、社会资本和技术资本。同时，企业的基因会随着环境的变化而不断地进行着调整和整合，以适应竞争的需要。实证分析的结果证实了笔者的判断。     

A Semiparametric Method for Valuing Residential Locations: Application to Automated Valuation

This paper is motivated by automated valuation systems, which would benefit from an ability to estimate spatial variation in location value. It develops theory for the local regression model (LRM), a semiparametric approach to estimating a location value surface. There are two parts to the LRM: (1) an ordinary least square (OLS) model to hold constant for interior square footage, land area, bathrooms, and other structural characteristics; and (2) a non-parametric smoother (local polynomial regression, LPR) which calculates location value as a function of latitude and longitude. Several methods are used to consistently estimate both parts of the model. The LRM was fit to geocoded hedonic sales data for six towns in the suburbs of Boston, MA. The estimates yield substantial, significant and plausible spatial patterns in location values. Using the LRM as an exploratory tool, local peaks and valleys in location value identified by the model are close to points identified by the tax assessor, and they are shown to add to the explanatory power of an OLS model. Out-of-sample MSE shows that the LRM with a first-degree polynomial (local linear smoothing) is somewhat better than polynomials of degree zero or degree two. Future applications might use degree zero (the well-known NW estimator) because this is available in popular commercial software. The optimized LRM reduces MSE from the OLS model by between 5 percent and 11 percent while adding information on statistically significant variations in location value.     

我国商业银行业公司治理结构与经营绩效的实证研究

通过对8家股份制商业银行的公司治理结构与经营绩效的关系做回归分析,结果显示:经营绩效与董事会规模正相关;与第一大股东持股比例负相关;与前5大股东控制权分散度正相关。因此,国有商业银行应当适度增大董事会规模、明晰产权关系、强化监事会职能、加强管理层激励与信息透明度。     

我国股份制商业银行薪酬激励有效性研究

我国股份制商业银行薪酬激励存在一些不足，原因在于薪酬激励的自身因素和薪酬环境因素两方面。自身因素主要包括忽视长期行为激励、缺乏同业竞争优势、轻视普通员工激励；环境因素主要有银行治理结构不完善、市场非充分有效、法规制度不健全等。本文通过对商业银行高管人员及普通员工的薪酬与业绩相关性进行实证检验，认为我国商业银行薪酬激励基本上是有效的，但是其有效性并不十分理想。据此，本文提出了提升我国商业银行薪酬激励有效性的具体建议：从微观层面看，优化薪酬激励机制：从中观层面看，强化商业银行治理结构；从宏观层面看，改善外部相关环境。     

The Effects of Attributes on the Repeat Sales Pattern of Residential Property in Hong Kong

There has been copious research work on the development of house price models and the construction of house price indices. However, results in some studies revealed that the accuracy of such indices could be subject to selection bias when using only information from a sample of sold properties to estimate value movements for the entire housing stock. In particular, estimated house price appreciation is usually systematically higher among properties that change hands more frequently. It therefore suggests that the determination of important factors affecting the transaction frequency or intensity of a housing unit should be a more fundamental research question. This paper examines the possible factors that determine the popularity of residential unit by means of a repeated sales pattern. The Poisson regression model and event history analysis techniques are employed to assess the effect of attributes on transaction frequency and intensity. The event history analyses technique can take account of transaction-specific as well as time-dependent covariates, and therefore is recommended for analyzing repeated sales data in a real estate market. All transaction records during the period 1993–2000 from the Land Registry of one of the most popular residential estates in Hong Kong were used to illustrate the method. Unlike a response to favorable transaction price, good quality units do not necessarily inherently display a high transaction frequency. Rather, units of average quality are more likely to be transactionally active.