大中专在校学生考试分数的正态化线性转换

本文分析了考试记分方法中传统的绝对百分体制的缺陷，给出了一种正态化转换分数的算法，较适合于在校大中专学生学习水平的横向评价。     

一般Gauss－Markov模型下一般可估函数的线性充分性和线性完全性

发展了Ｂａｋｓａｌａｒｙ和Ｄｒｙｇａｓ提出的一般Ｇａｕｓｓ－Ｍａｒｋｏｖ模型中线性充分性、最小充分性和完全性的概念，用Ｒａｏ的最小二乘统一理论，给出了这些概念的刻划定理。     

核准制IPO定价实证分析

本文用信息经济学的理论和计量经济学的实证分析方法研究了我国核准制下的新股定价问题 ,指出市场主体决策时的信息差异是造成新股发行价和上市价相背离的主要原因 ,并构建多元回归模型对这一假设进行了检验。     

Psychological taxing in Flemish municipalities

We analyse whether the psychological pricing in the private sector has a public sector counterpart in tax policy. Analysing the main theoretical arguments for the existence of price points, and applying them to the public sector, suggests that psychological taxing reveals itself by the use of non-0 ending tax rates. The tax rate endings of the local income taxes, which are set by 308 Flemish municipalities in the fiscal year 1998, suggests the presence of psychological taxing. Non-0 endings occur more frequently in municipalities where demand for public policy is more elastic (and where, therefore, the benefits to the politicians from setting a tax just below a tax point is higher). The pre-tax income inequality and the level of the tax rate positively affect psychological taxing. The latter effect is reinforced in those municipalities where the existing tax rate is above the average tax rate in neighbouring municipalities and below their neighbours’ minimum, although this effect has a limited effect and is offset the further below the minimum the tax is set.     

Robust confidence interval for a residual standard deviation

The residual standard deviation of a general linear model provides information about predictive accuracy that is not revealed by the multiple correlation or regression coefficients. The classic confidence interval for a residual standard deviation is hypersensitive to minor violations of the normality assumption and its robustness does not improve with increasing sample size. An approximate confidence interval for the residual standard deviation is proposed and shown to be robust to moderate violations of the normality assumption with robustness to extreme non-normality that improves with increasing sample size.     

Uncertainty Analysis for Computer Model Projections of Hurricane Losses

Projecting losses associated with hurricanes is a complex and difficult undertaking that is wrought with uncertainties. Hurricane Charley, which struck southwest Florida on August 13, 2004, illustrates the uncertainty of forecasting damages from these storms. Due to shifts in the track and the rapid intensification of the storm, real-time estimates grew from 2 to 3 billion dollars in losses late on August 12 to a peak of 50 billion dollars for a brief time as the storm appeared to be headed for the Tampa Bay area. The storm hit the resort areas of Charlotte Harbor near Punta Gorda and then went on to Orlando in the central part of the state, with early poststorm estimates converging on a damage estimate in the 28 to 31 billion dollars range. Comparable damage to central Florida had not been seen since Hurricane Donna in 1960. The Florida Commission on Hurricane Loss Projection Methodology (FCHLPM) has recognized the role of computer models in projecting losses from hurricanes. The FCHLPM established a professional team to perform onsite (confidential) audits of computer models developed by several different companies in the United States that seek to have their models approved for use in insurance rate filings in Florida. The team's members represent the fields of actuarial science, computer science, meteorology, statistics, and wind and structural engineering. An important part of the auditing process requires uncertainty and sensitivity analyses to be performed with the applicant's proprietary model. To influence future such analyses, an uncertainty and sensitivity analysis has been completed for loss projections arising from use of a Holland B parameter hurricane wind field model. Uncertainty analysis quantifies the expected percentage reduction in the uncertainty of wind speed and loss that is attributable to each of the input variables.     

FDI对我国经济增长的实证分析

就外商直接投资(FD I)对我国经济增长的影响这一焦点问题,国内学者从定性采用多指标和从定量使用传统的基于普通最小二乘法(O LS)下的计量经济模型两个方面进行了比较详尽的研究。本文在现有文献基础上进行了尝试性创新,使用O LS法进行回归分析时,考虑了数据的平稳性问题,进行了数据的平稳性检验,并运用科克伦-奥克特(C ochrane-O rcutt法)方法纠正了可能存在的虚假回归现象,从而使估计结果更加稳健,结论也更加合理。     

Residual analysis for spatial point processes (with discussion)

Summary.  We define residuals for point process models fitted to spatial point pattern data, and we propose diagnostic plots based on them. The residuals apply to any point process model that has a conditional intensity; the model may exhibit spatial heterogeneity, interpoint interaction and dependence on spatial covariates. Some existing ad hoc methods for model checking (quadrat counts, scan statistic, kernel smoothed intensity and Berman's diagnostic) are recovered as special cases. Diagnostic tools are developed systematically, by using an analogy between our spatial residuals and the usual residuals for (non-spatial) generalized linear models. The conditional intensity λ plays the role of the mean response. This makes it possible to adapt existing knowledge about model validation for generalized linear models to the spatial point process context, giving recommendations for diagnostic plots. A plot of smoothed residuals against spatial location, or against a spatial covariate, is effective in diagnosing spatial trend or co-variate effects. Q – Q -plots of the residuals are effective in diagnosing interpoint interaction.     

一类多元Gauss-Weierstrass算子线性组合的加权逼近

该文给出了一类多元Gauss-Weierstrass算子线性组合加Jacobi权在一致逼近下的正、逆定理和逼近阶的特征刻划     

基于贝叶斯神经网络的非参数回归

提高神经网络模型推广能力的关键是控制模型的复杂度。该文探索了贝叶斯神经网络的非参数回归的建模方法,通过融入模型参数的先验知识,在给定数据样本及模型假设下进行后验概率的贝叶斯推理,使用马尔可夫链蒙特卡罗算法来优化模型控制参数,实现了对神经网络模型中不同部分复杂度的控制,获得了模型参数的后验分布及预测分布。在5个含噪二维函数回归问题上的应用显示了模型的复杂度能根据数据的复杂度而自适应调整,并给出了较好的预测结果。