“泰山石敢当”原为“泰巖堂”或“泰巖皇”——“泰山皇玺”考识

“泰山石敢当”词义难解,“石敢当”之人于古史无征。通过“泰山皇玺”的考证,得知“泰山石敢当”实为“泰巖 當”误写讹传,而“當”字应释为“堂”或“皇”,因而“泰山石敢当”原为“泰巖堂”或“泰巖皇”。“泰巖堂”即“泰山明堂”, “泰巖皇”即“泰山皇”。“泰巖堂”石碑,为古时“镇堂神石”,“泰巖皇”为原始“泰山神”。“泰山神”原应为“神龙禹皇”。     

线性模型参数β的均匀压缩估计

对一般线性模型中参数β的最小二乘估计和岭估计进行了修正;把岭估计中各分量的非均匀压缩修为均匀压缩,从而得到了β的一种均匀压缩估计＾βａ,并给出了具体的求法和适用范围     

On the shrinkage of local linear curve estimators

Local linear curve estimators are typically constructed using a compactly supported kernel, which minimizes edge effects and (in the case of the Epanechnikov kernel) optimizes asymptotic performance in a mean square sense. The use of compactly supported kernels can produce numerical problems, however. A common remedy is ridging, which may be viewed as shrinkage of the local linear estimator towards the origin. In this paper we propose a general form of shrinkage, and suggest that, in practice, shrinkage be towards a proper curve estimator. For the latter we propose a local linear estimator based on an infinitely supported kernel. This approach is resistant against selection of too large a shrinkage parameter, which can impair performance when shrinkage is towards the origin. It also removes problems of numerical instability resulting from using a compactly supported kernel, and enjoys very good mean squared error properties.     

基于GRPLS 回归的 中国经济增长影响因素分析

构造一种新的方法———岭- 偏最小二乘回归方法(它既有效消除了因素变量之间的多 重共线性,又克服了传统方法的不足,且使模型更加稳健,具有更强的预测和分析能力) ;并运 用广义岭- 偏最小二乘回归方法分析了我国经济增长的影响因素,为我国制订持续、快速增长 的经济政策提供了有益的参考.     

Optimal determination of the parameters of some biased estimators using genetic algorithm

In this paper, some new algorithms for estimating the biasing parameters of the ridge, Liu and two-parameter estimators are introduced with the help of genetic algorithm (GA). The proposed algorithms are based on minimizing some statistical measures such as mean square error (MSE), mean absolute error (MAE) and mean absolute prediction error (MAPE). At the same time, the new algorithms allow one to keep the condition number and variance inflation factors to be less than or equal to ten by means of the GA. A numerical example is presented to show the utility of the new algorithms. In addition, an extensive Monte Carlo experiment is conducted. The numerical findings prove that the proposed algorithms enable to eliminate the problem of multicollinearity and minimize the MSE, MAE and MAPE.     

Adaptation of the jackknifed ridge methods to the linear mixed models

The purpose of this article is to obtain the jackknifed ridge predictors in the linear mixed models and to examine the superiorities, the linear combinations of the jackknifed ridge predictors over the ridge, principal components regression, r?k class and Henderson's predictors in terms of bias, covariance matrix and mean square error criteria. Numerical analyses are considered to illustrate the findings and a simulation study is conducted to see the performance of the jackknifed ridge predictors.     

试述北魏宗室阶层的犯罪类型

北魏宗室阶层是政权的柱石、统治集团的核心,在政治和社会领域里享受充分的特权,北魏时期的法律反映了宗室特权的主要方面.由于拓跋鲜卑历经由游牧部落向文明国家阶段的跃进,宗室成员在身份角色的转换过程中不可避免地会与国家法制和名教礼法发生冲突,表现为具体的犯罪行为.北魏宗室非违主要有政治犯罪、职务犯罪、刑事犯罪和悖逆纲常伦理四类.各类罪行的时间分布特点和惩治力度集中反映出宗室生存境遇的改变和王朝政局的走势.     

Nonparametric Shrinkage Estimation for Aalen's Additive Hazards Model

Aalen's nonparametric additive model in which the regression coefficients are assumed to be unspecified functions of time is a flexible alternative to Cox's proportional hazards model when the proportionality assumption is in doubt. In this paper, we incorporate a general linear hypothesis into the estimation of the time‐varying regression coefficients. We combine unrestricted least squares estimators and estimators that are restricted by the linear hypothesis and produce James‐Stein‐type shrinkage estimators of the regression coefficients. We develop the asymptotic joint distribution of such restricted and unrestricted estimators and use this to study the relative performance of the proposed estimators via their integrated asymptotic distributional risks. We conduct Monte Carlo simulations to examine the relative performance of the estimators in terms of their integrated mean square errors. We also compare the performance of the proposed estimators with a recently devised LASSO estimator as well as with ridge‐type estimators both via simulations and data on the survival of primary billiary cirhosis patients.     

The exact properties of the lawless and wang operational ridge regression estimator in a misspecified regression equation

The exact properties of the Lawless and Wang Operational Ridge Regression estimator are derived in the context of a misspecified regression equation.     

Ridge Regression: Degrees of Freedom in the Analysis of Variance

It appears to be common practice with ridge regression to obtain a decomposition of the total sum of squares, and assign degrees of freedom, according to established least squares theory. This discussion notes the obvious fallacies of such an approach, and introduces a decomposition based on orthogonality, and degrees of freedom based on expected mean squares, for non-stochastic k.