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??This paper develops a covariate-adjusted precision matrix estimation using a two-stage estimation procedure. Firstly, we identify the relevant covariates that affect the means by a joint l_1 penalization. Then, the estimated regression coefficients are used to estimate the mean values in a multivariate sub-Gaussian model in order to estimate the sparse precision matrix through a Lasso penalized D-trace loss. Under some assumptions, we establish the convergence rate of the precision matrix estimation under different norms and demonstrate the sparse recovery property with probability converging to one. Simulation shows that our methods have the finite-sample performance compared with other methods.     

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In this paper an efficient estimation methodology for the partially linear models with random effects is proposed. For this, we use the generalized least square estimate (GLSE) and the B-splines methods to estimate the unknowns, and employ the penalized least square method to obtain the estimators of the random effects item. Further, we also consider the estimation for the variance components. Compared with the existing methods, our proposed methodology performs well. The asymptotic properties of the estimators are obtained. A simulation study is carried out to assess the performance of our proposed methodology.     

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本文研究随机约束下线性回归模型中, 回归系数的加权混合估计与最小二乘估计的相对效率, 并且给出了相对效率的上下界限. 最后我们给出了一个例子来验证我们的理论结果.     

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??We consider a Markov switching exponential Levy model in which the
underlying economy switches between a finite number of states. The switching is modeled by a
hidden Markov chain. We explore the link between options prices in Markov switching exponential
Levy models and the related partial integro-differential equations in the case of European
options.     

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This paper concerns with the estimation of a fixed effects panel data partially linear regression model with the idiosyncratic errors being an autoregressive process. For fixed effects short time series panel data, the commonly used autoregressive error structure fitting method will not result in a consistent estimator of the autoregressive coefficients. Here we propose an alternative estimation and show that the resulting estimator of the autoregressive coefficients is consistent and this method is workable for any order autoregressive error structure. Moreover, combining the B-spline approximation, profile least squares dummy variable (PLSDV) technique and consistently estimated the autoregressive error structure, we develop a weighted PLSDV estimator for the parametric component and a weighted B-spline series (BS) estimator for the nonparametric component. The weighted PLSDV estimator is shown to be asymptotically normal and more asymptotically efficient than the one which ignores the error autoregressive structure. In addition, this paper derives the asymptotic bias of the weighted BS estimator and establish its asymptotic normality as well. Simulation studies and an example of application are conducted to illustrate the finite sample performance of the proposed procedures.     

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VaR风险度量在金融、保险中有重要的应用. 本文建立了贝叶斯模型, 在某种损失函数下研究了VaR风险度量的贝叶斯估计. 证明了指数-伽马分布下贝叶斯估计的强相合性和渐近正态性, 最后利用数值模拟的方法验证了不同样本容量下估计的收敛速度.     

基于Copula函数的屏蔽数据并-串联系统可靠性分析

讨论了失效相依屏蔽数据系统的可靠性分析问题.通过引入Copula函数描述部件寿命变量之间的相依关系,建立屏蔽数据并-串联系统可靠性模型,推导出并-串联系统的一些概率结果.在此基础上,基于逐步Ⅰ型混合截尾的系统失效数据,获得了模型参数及可靠性指标的极大似然估计和bootstrap区间估计.最后,运用蒙特卡罗模拟验证了方法的可行性和有效性.     

基于Copula函数的应力-强度模型可靠度的非参数估计

本文研究了相关的应力变量和强度变量在右删失的情形下,应力-强度模型可靠度的非参数估计.其中变量之间的相关关系采用常见的Farlie-Gurnbel-Morgenstern copula函数和Clayton copula函数来度量.采用经验过程的理论,本文建立了所提出估计量的相合性及渐近正态性.数值模拟的结果表明所提出的方法在有限样本下表现良好.本文所提出的方法在实际中有广泛的应用前景.     

Lω-空间的ωθ-连通性

研究了Lω-空间的ωθ-连通性问题。利用Lω-空间的的ωθ-闭集和ωθ-连通集等概念,系统讨论了这些概念的特征性质,证明了Lω-空间的ωθ-连通性具有同胚不变性等性质。     

Lω-空间的ωδ-紧性

在Lω-空间中借助βα-ωδ-开覆盖,定义了Lω-空间的ωδ-紧性,ωδ-基与ωδ-子基,并证明了ωδ-紧性被连续的Zadeh型函数所保持,Tychonoff乘积定理也成立.     

基于Copula理论的装甲车辆齿轮传动箱系统的可靠性分析

应用Copula函数研究了某型装甲车辆齿轮传动箱的系统可靠性问题,得出了系统的可靠度函数和失效率,并与独立性下的可靠度函数进行比较,通过分析两种方法下可靠度的变化趋势,得出基于Copula函数的可靠度函数更接近于实际情况.     

基于Copula函数和非参数估计的尾部相关性

给出基于Copula函数的尾部相关性的定义和性质,采用非参数方法估计尾部相关系数.结合数据得出上证指数和深圳指数的尾部相关系数和对应图形比较,可知两种股票的上尾比下尾相关性强.此相关系数反映了上证指数与深圳指数在极端值处同时小于或同时大于某个数值的概率大小.     

非参数核密度估计与Copula

本文提出非参数核密度估计-ML方法来估计Copula函数中的未知参数;再由统计检验推断得到能较好描述金融资产之间非线性相关结构的Copula。实证分析表明:可以利用Clayton Copula、Gumbel Copula来描述A股市场上证指数与深证成指之间的非线性相关结构.     

操作风险计量研究——基于贝叶斯Copula方法

商业银行操作风险的计量存在两个重要的问题,一个问题是损失数据缺乏,另一个问题是各部分之间的风险相关问题.结合Bayes估计和Copula函数解决了上述两个问题并基于中国商业银行操作风险的损失数据对对操作风险的计量进行了实证分析.实证分析的结果表明无论考虑风险相关与否,基于极大似然估计的VaR与基于Bayes估计的VaR具有一定的差距.     

Estimation and Bootstrap with Censored Data in Fixed Design Nonparametric Regression

We study Beran's extension of the Kaplan-Meier estimator for thesituation of right censored observations at fixed covariate values. Thisestimator for the conditional distribution function at a given value of thecovariate involves smoothing with Gasser-Müller weights. We establishan almost sure asymptotic representation which provides a key tool forobtaining central limit results. To avoid complicated estimation ofasymptotic bias and variance parameters, we propose a resampling methodwhich takes the covariate information into account. An asymptoticrepresentation for the bootstrapped estimator is proved and the strongconsistency of the bootstrap approximation to the conditional distributionfunction is obtained.     

基于Spearman的rho的Copula参数模型的选择

从Spearman的rho与Kendall的tau的关系入手,讨论了一类二元Copula参数模型的选择问题.由于这类二元Copula参数模型的Spearman的rho与Kendall的tau存在某种函数关系,模型选择问题转化为了曲线拟合检验问题.对于正态Copula、Frank-Copula,FGM-Copula、B11-Copula等这类Copula参数模型,说明了两种情况下进行模型选择的方法,并对中国股市的上证指数与深证综指作了实证分析,结果表明两者存在着较强的正相关性,相关性模型选取B11-Copula参数模型最合适.     

基于Copula和极值理论的在险价值度量

针对传统孤立使用GJR模型、极值理论、Copula理论进行风险分析的不足,把GJR模型、极值理论和Copula理论有机的结合起来,给出了基于Copula和极值理论的投资组合VaR的测度方法.首先利用GJR模型刻画单个资产收益率中的自相关和异方差现象,获得近似独立同分布的新息序列,再分别应用高斯核估计的方法、极值理论拟合新息序列的分布函数的内部和两尾,利用Copula函数有效捕抓了市场之间的波动溢出效应,最后使用Monte Carlo模拟法,计算出投资组合的VaR值.实证结果表明,基于Copula和极值理论的VaR度量方法比历史模拟法更有效.     

一类非参数回归函数估计的性质

设Z_(11),z_(12),…,Z_是在固定点(x_i,y_1),1≤≤n_1,1≤j≤n_2,的n_1n_2个观察值,适合模型 Z_(ij)=g(x_i,y_j)+ε_(ij),1≤i≤n_1,1≤j≤n_2。(1) 本文给出了g的一种估计并讨论了估计的性质。     

混合序列矩不等式和非参数估计

对p-混合、(?)-混合序列给出两个矩不等式,它们在加权和序列研究中比邵启满在[6]、[7]中给出的矩不等式更实用.作为应用,这里讨论非参数递归密度核估计的强收敛速度和非参数回归函数加权核估计的强相合性,获得较好结论.     

应力、强度均服从瑞利（Rayleigh）分布的可靠性分析

在应力—强度干涉模型中,当应力、强度均服从瑞利(Rayleigh)分布:R(x│β)={2βxexp[(-xβ2)],x00x<0,参数未知的情况下,讨论了在给定验前分布情况下的可靠性R=P(Y相似文献