Deletion residuals in the detection of heterogeneity of variances in linear regression

The heterogeneity of error variance often causes a huge interpretive problem in linear regression analysis. Before taking any remedial measures we first need to detect this problem. A large number of diagnostic plots are now available in the literature for detecting heteroscedasticity of error variances. Among them the ‘residuals’ and ‘fits’ (R–F) plot is very popular and commonly used. In the R–F plot residuals are plotted against the fitted responses, where both these components are obtained using the ordinary least squares (OLS) method. It is now evident that the OLS fits and residuals suffer a huge setback in the presence of unusual observations and hence the R–F plot may not exhibit the real scenario. The deletion residuals based on a data set free from all unusual cases should estimate the true errors in a better way than the OLS residuals. In this paper we propose ‘deletion residuals’ and the ‘deletion fits’ (DR–DF) plot for the detection of the heterogeneity of error variances in a linear regression model to get a more convincing and reliable graphical display. Examples show that this plot locates unusual observations more clearly than the R–F plot. The advantage of using deletion residuals in the detection of heteroscedasticity of error variance is investigated through Monte Carlo simulations under a variety of situations.     

Non‐parametric Threshold Estimation for Models with Stochastic Diffusion Coefficient and Jumps

Abstract. We consider a stochastic process driven by diffusions and jumps. Given a discrete record of observations, we devise a technique for identifying the times when jumps larger than a suitably defined threshold occurred. This allows us to determine a consistent non‐parametric estimator of the integrated volatility when the infinite activity jump component is Lévy. Jump size estimation and central limit results are proved in the case of finite activity jumps. Some simulations illustrate the applicability of the methodology in finite samples and its superiority on the multipower variations especially when it is not possible to use high frequency data.     

一般工科院校非英语专业大学生英语学习策略调查

本文运用定量研究的方法,调查了南昌航空工业学院509名非英语专业本科生英语学习策略.结果发现,一般院校非英语专业本科生在管理观念和策略、母语观念和策略功能操练观念和策略上存在很大问题,投入于英语学习的时间严重不足. 据此本文提出大学英语教学应以策略培训为基础,用英语教学,培养英语思维能力,强化学习动机.     

“东边不亮,西边亮”——“11·21”包头空难在美国

2004年11月21日,东方航空公司一架从包头直飞上海的客机距机场不远的南海公园发生空难。部分遇难者家属不满东航公司提出的赔偿标准,历经波折在美国提起诉讼,继而又转回国内。＂11.21＂包头空难的遇难者家属在不能得到满意的赔偿情况下,和律师团达成风险代理协议,从而减轻了家属们的诉讼负担。这给我们以启示,应更多地了解外面的法律制度,借鉴以完善我国的立法之外,还要利用外国司法资源为我国受害者提供救济。     

光盘扫描高斯光束的研究

讨论了扫描光束对高斯参数扫描光点强度分布的影响；给出了高斯参数与中心峰值强度及光点直径的定量关系；展示出焦平面上的三维光强分布图；提出了光盘存贮中高斯参数的选择依据；有效利用无能，获得高性能扫描光点。     

考虑并行制造的云服务外购件供应商组合优选

云制造环境下,为解决并行结构下外购件供应商组合优选难的问题,在考虑并行制造的前提下,课题组以时间、成本、可靠性和灵活性为优化目标,构建了云服务多目标组合优选模型。提出了一种改进的非支配排序遗传算法（NSGA Ⅲ TS）对模型进行求解,该算法采用反向学习策略提高初始解的质量;而后使用融合邻域搜索（采用交换、反转和插入3种方式）和禁忌搜索算法的策略加强局部搜索,提高算法后期局部寻优能力;对得到的一系列优选供应商组合综合评价,在考虑实际需求的基础上为需求方选出了最优供应商组合及备选方案。通过实际案例验证了该模型的有效性和算法的可行性。     

论我国酌定不起诉权行使的条件

从立法机关对检察机关的酌定不起诉权的态度和《刑法》的相关规定分析,\"犯罪情节轻微\"是我国检察机关行使酌定不起诉权的前提条件,而将\"犯罪情节\"理解为量刑情节则有助于充分实现酌定不起诉权的积极功能。     

A Note on the Large Sample Properties of Estimators Based on Generalized Linear Models for Correlated Pseudo‐observations

Pseudo‐values have proven very useful in censored data analysis in complex settings such as multi‐state models. It was originally suggested by Andersen et al., Biometrika, 90, 2003, 335 who also suggested to estimate standard errors using classical generalized estimating equation results. These results were studied more formally in Graw et al., Lifetime Data Anal., 15, 2009, 241 that derived some key results based on a second‐order von Mises expansion. However, results concerning large sample properties of estimates based on regression models for pseudo‐values still seem unclear. In this paper, we study these large sample properties in the simple setting of survival probabilities and show that the estimating function can be written as a U‐statistic of second order giving rise to an additional term that does not vanish asymptotically. We further show that previously advocated standard error estimates will typically be too large, although in many practical applications the difference will be of minor importance. We show how to estimate correctly the variability of the estimator. This is further studied in some simulation studies.