Quantile regression for robust estimation and variable selection in partially linear varying-coefficient models

In this paper, we develop a new estimation procedure based on quantile regression for semiparametric partially linear varying-coefficient models. The proposed estimation approach is empirically shown to be much more efficient than the popular least squares estimation method for non-normal error distributions, and almost not lose any efficiency for normal errors. Asymptotic normalities of the proposed estimators for both the parametric and nonparametric parts are established. To achieve sparsity when there exist irrelevant variables in the model, two variable selection procedures based on adaptive penalty are developed to select important parametric covariates as well as significant nonparametric functions. Moreover, both these two variable selection procedures are demonstrated to enjoy the oracle property under some regularity conditions. Some Monte Carlo simulations are conducted to assess the finite sample performance of the proposed estimators, and a real-data example is used to illustrate the application of the proposed methods.     

Estimation of P{X < Y} for geometric—exponential model based on complete and censored samples

This article deals with the estimation of R = P{X < Y}, where X and Y are independent random variables from geometric and exponential distribution, respectively. For complete samples, the MLE of R, its asymptotic distribution, and confidence interval based on it are obtained. The procedure for deriving bootstrap-p confidence interval is presented. The UMVUE of R and UMVUE of its variance are derived. The Bayes estimator of R is investigated and its Lindley's approximation is obtained. A simulation study is performed in order to compare these estimators. Finally, all point estimators for right censored sample from the exponential distribution, are obtained.     

Estimation of parameters of inverse Weibull distribution and application to multi-component stress-strength model

The problem of estimation of the parameters of two-parameter inverse Weibull distributions has been considered. We establish existence and uniqueness of the maximum likelihood estimators of the scale and shape parameters. We derive Bayes estimators of the parameters under the entropy loss function. Hierarchical Bayes estimator, equivariant estimator and a class of minimax estimators are derived when shape parameter is known. Ordered Bayes estimators using information about second population are also derived. We investigate the reliability of multi-component stress-strength model using classical and Bayesian approaches. Risk comparison of the classical and Bayes estimators is done using Monte Carlo simulations. Applications of the proposed estimators are shown using real data sets.     

花东甲骨中的否定词

对花园庄东地甲骨中的八个否定副词“不、弗、非、毋、勿、弱、妹、亡”，本文进行了整理，并简单探讨了“亡”的副词功能。     

甲骨文家谱刻辞研究——对《库、方二氏藏甲骨卜辞》第1506片甲骨的考察

《库、方二氏藏甲骨卜辞》一书中第1506片甲骨刻辞涉及到所谓的“家谱刻辞”,对其真伪的研究,在过去的百年甲骨学史上,一直是甲骨学界和殷商史研究中争议不决的热点问题。而且,明显地形成了观点截然对立的以于省吾先生和胡厚宣先生为代表的真伪两派学术阵营。本文在认真考证和分析研究的基础上,得出了这片甲骨为真品的结论。     

商代祭祀活动中的“反本归宗”

"反本归宗"是商代宗教和礼制的基本原则之一.卜辞中的"报",是指祭祀.卜辞中有"即宗"的术语,"宗"指甲与辛日.     

历组卜辞“日月有食”“日、月有■”卜骨的时代位序

历组卜辞中的“日月有食”卜骨根据历组卜辞的分类分群,从其字体特征看,应属历组二Ｂ类丙群,时代上应属祖庚后半期。“日、月有”卜骨分“日有”和“月有”,前者次序由历组卜辞二Ｂ类乙群到二Ｂ类丙群;后者属历组卜辞二Ｂ类乙群。两者时代为祖庚中晚期。     

基于Bayes推理的灾害演化GERT网络模型研究

灾害、衍生和次生灾害及其相互耦合使得灾情恶化,作为灾害对抗力量的抢险、避险、控制的救险等措施使得灾害向灾情减轻的方向转化,本文对灾害的这一动态演化过程进行描述,建立一种综合考虑灾害的自然演化与抢险救灾行动的基于Bayes推理的灾害演化GERT(Graph Evaluation and Review Technique)网络模型;把GERT网络方法和贝叶斯推理工具相结合,根据获得的新信息,对GERT网络中活动参数进行动态修正,对灾害的演化路径,各种主要状态的演化概率和时间进行动态预测、预警与评价;对衍生与次生灾害、抢险救灾行动等外界行为对系统演化的影响进行定性与定量分析,并给出定量评价结论。本文提供了灾害演化定性与定量结合的分析框架与工具,揭示灾害演化机理,为灾害发展态势的预测、预警与评价提供了一种新的研究方法和研究思路。