集合的$\varOmega$-凸性及其基础性质[英文]

作为集合凸性概念的一种推广以及统一凸性与近似（nearly）凸性等概念的一种尝试, 本文引入集合$\varOmega$-凸性的概念, 并对$\varOmega$-凸集合的性质进行初步的研究. 另外, 本文还研究了一些常见变换与集合运算的保$\varOmega$-凸性质.     

某类退化椭圆型方程在W^1,p中广义解的局部正则性

设是一个有界区域,考虑问题: D_i(g(|Du|~2)|Du|~(p-2)D_iu)=0,x∈Ω其中g(0)=0;当t>0时,g(t)>0.加上g(t)必须满足的条件.利用这些条件,我们获得上述退化椭圆型方程的广义解的局部正则性。 在文中,我们运用了逼近法和证明主要引理的一些技巧。 最后,我们给出两个具体例子。     

具有奇性的常微分方程的边值问题

本文对具有奇性的常微分方程的边值问题（ｔ）ｙ'=(ｔ，ｙ，ｙ＇），ｙ（０）＝ａ，ｙ（１）＝ｂ建立了Ｎａｇｕｍｏ型存在定理．     

耦合动力系统解的延拓性

近年来,人们对耦合动力系统的同步行为进行了大量的研究.在数学上,同步可以定义为:在耦合或者外力的作用下,两个或多个动力系统的行为随着时间趋于无穷而趋于一种共同状态的过程.因此,在讨论同步行为之前,必须首先解决一个关于解的延拓性的问题:对于给定的初始值,耦合系统的解是否可以延拓到正无穷,即解足否可以在无穷区间[0,+∞)上存在?提出了一个一般的耦合动力系统模型,并且证明了在QUAD假设下,该一般模型的解在区间[0,+∞)上存在.     

K-drop凸空间中的性质

为了阐明何为K 强光滑空间的对偶空间 ,本文定义了K drop凸空间并且讨论了该空间的一些性质。同时借助K 强光滑空间的一个等价定义 ,证明了K drop凸空间与K 强光滑空间是对偶空间。文章最后用单位圆的切片给出了K drop凸空间的一等价命题 ,进而建立了K drop凸空间与drop性之间的关系。     

高阶混合型泛函微分方程的振动性

本文讨论高阶微分差分方程的振动性问题,这里Pi,τi,qi,σi是常数,偏差τi,σi可正、可负,q₁,…,q_m同号.我们证明其一切解振动的充分必要条件是它的特征方程无实根.     

一类二阶非线性摄动微分方程的振动性与渐近性

研究了一类二阶非线性摄动微分方程解的振动性质和渐近性质,建立了两个新的振动性与渐近性定理,推广和改进了已有的一些结果.     

非线性中立型时滞差分方程解的渐近性

本文考虑非线性中立型差分方程△m(Xn-m 1 Pn-m 1Xn-m 1-k) f(n,Xn-l) 0解的振动性和渐近性,所得结果推广了文献中的有关定理．     

二阶奇异椭圆方程的Dirichlet问题

对二阶奇异椭圆方程-△u 1/uα-λuP=0的Dirichlet问题(λ＞0,0＜P＜1)证明了当α≥1时无解存在,当0＜α＜1时存在极小解;并对较一般的奇异方程给出了一个存在性结果.     

关于Katai的一个猜想的证明

本文证明了：如果实值加性函数ｆ（ｎ）满足条件‖ｆ（ｎ＋１）－ｆ（ｎ）‖＝ｏ（１），（ｎ→∞）这里‖‖表示一实数与最近的整数的距离，则一定有某常数ｃ使ｆ（ｎ）一ｃｌｏｇｎ为整数值加性函数．这证实了ＫａｔａｉⅠ的一个猜想．     

A notion of an obstructive residual likelihood

Summary A new notion of an obstructive residual likelihood is proposed and explored. Examples where the conditional maximum likelihood estimator is preferable to the unconditional maximum likelihood estimator are discussed. In these examples the residual likelihood can be obstructive in deriving a preferable estimator, when the maximum likelihood criterion is applied. This notion is different from a similar notion ancillarity, which simply emphasizes that a residual likelihood is un-informative. The Institute of Statistical Mathematics     

非线性模型中的拟似然估计和约束拟似然估计

该文证明了,在非线性回归模型中,若以均方误差或均方误差矩阵为标准,拟似然估计是正则广义拟似然估计类中的最优估计,并讨论了拟得分函数最优性与拟似然估计最优性的关系.为改进拟似然估计,该文提出了一种约束拟似然估计,并证明了约束拟似然估计比拟似然估计有较小的均方误差.     

Adjusted Empirical Likelihood and its Properties

Computing a profile empirical likelihood function, which involves constrained maximization, is a key step in applications of empirical likelihood. However, in some situations, the required numerical problem has no solution. In this case, the convention is to assign a zero value to the profile empirical likelihood. This strategy has at least two limitations. First, it is numerically difficult to determine that there is no solution; second, no information is provided on the relative plausibility of the parameter values where the likelihood is set to zero. In this article, we propose a novel adjustment to the empirical likelihood that retains all the optimality properties, and guarantees a sensible value of the likelihood at any parameter value. Coupled with this adjustment, we introduce an iterative algorithm that is guaranteed to converge. Our simulation indicates that the adjusted empirical likelihood is much faster to compute than the profile empirical likelihood. The confidence regions constructed via the adjusted empirical likelihood are found to have coverage probabilities closer to the nominal levels without employing complex procedures such as Bartlett correction or bootstrap calibration. The method is also shown empirical likelihood.     

Empirical likelihood for heteroscedastic partially linear models

We make empirical-likelihood-based inference for the parameters in heteroscedastic partially linear models. Unlike the existing empirical likelihood procedures for heteroscedastic partially linear models, the proposed empirical likelihood is constructed using components of a semiparametric efficient score. We show that it retains the double robustness feature of the semiparametric efficient estimator for the parameters and shares the desirable properties of the empirical likelihood for linear models. Compared with the normal approximation method and the existing empirical likelihood methods, the empirical likelihood method based on the semiparametric efficient score is more attractive not only theoretically but empirically. Simulation studies demonstrate that the proposed empirical likelihood provides smaller confidence regions than that based on semiparametric inefficient estimating equations subject to the same coverage probabilities. Hence, the proposed empirical likelihood is preferred to the normal approximation method as well as the empirical likelihood method based on semiparametric inefficient estimating equations, and it should be useful in practice.     

平衡增加的经验欧氏似然

经验(欧氏)似然方法是近年来非常流行的一种非参数统计方法.针对经验(欧氏)似然的凸包限制和计算复杂问题,本文借助Emerson和Owen (2009)所提出的平衡增加思想对经验欧氏似然进行修正,得到了平衡增加的经验欧氏似然.随后论文从理论和模拟两个方面进行了研究.理论上给出了该方法与经验欧氏似然检验函数之间的联系,即在固定的样本量n下随着添加点位置的连续变化,检验方法可以从简单的均值增加经验欧氏似然变化到经验欧氏似然检验;模拟结果显示,适当选取调整因子,平衡增加的经验欧氏似然相对于(调整)经验欧氏似然而言,在大多数情况下,其分布更接近于对应的极限分布.     

Jackknife-blockwise empirical likelihood methods under dependence

Empirical likelihood for general estimating equations is a method for testing hypothesis or constructing confidence regions on parameters of interest. If the number of parameters of interest is smaller than that of estimating equations, a profile empirical likelihood has to be employed. In case of dependent data, a profile blockwise empirical likelihood method can be used. However, if too many nuisance parameters are involved, a computational difficulty in optimizing the profile empirical likelihood arises. Recently, Li et al. (2011) [9] proposed a jackknife empirical likelihood method to reduce the computation in the profile empirical likelihood methods for independent data. In this paper, we propose a jackknife-blockwise empirical likelihood method to overcome the computational burden in the profile blockwise empirical likelihood method for weakly dependent data.     

The maximum full and partial likelihood estimators in the proportional hazard model

Summary The maximum full likelihood estimator in the proportional hazard model is explored in relation to the maximum partial likelihood estimator. In the scalar parameter case both the estimators have a common sign, and the absolute value of the former is strictly greater than that of the latter except for trivial cases. We point out also that the maximum full likelihood estimator after a simple modification of the likelihood equation provides a good approximation to the maximum partial likelihood estimator. Similar results are valid for the likelihood ratio tests. The Institute of Statistical Mathematics     

Pareto分布环境因子的估计及其应用

给出了Pareto分布环境因子的定义,讨论了在定数截尾样本下Pareto分布环境因子的极大似然估计和修正极大似然估计,并尝试把环境因子用于可靠性评估中.最后运用Monte Carlo方法对极大似然估计,修正极大似然估计和可靠性指标的均方误差(MSE),进行了模拟比较,结果表明修正极大似然估计优于极大似然估计且考虑环境因子的可靠性评估结果较好.     

The singly truncated normal distribution: A non-steep exponential family

This paper is concerned with the maximum likelihood estimation problem for the singly truncated normal family of distributions. Necessary and suficient conditions, in terms of the coefficient of variation, are provided in order to obtain a solution to the likelihood equations. Furthermore, the maximum likelihood estimator is obtained as a limit case when the likelihood equation has no solution.     

Parsimonious and Efficient Likelihood Composition by Gibbs Sampling

The traditional maximum likelihood estimator (MLE) is often of limited use in complex high-dimensional data due to the intractability of the underlying likelihood function. Maximum composite likelihood estimation (McLE) avoids full likelihood specification by combining a number of partial likelihood objects depending on small data subsets, thus enabling inference for complex data. A fundamental difficulty in making the McLE approach practicable is the selection from numerous candidate likelihood objects for constructing the composite likelihood function. In this article, we propose a flexible Gibbs sampling scheme for optimal selection of sub-likelihood components. The sampled composite likelihood functions are shown to converge to the one maximally informative on the unknown parameters in equilibrium, since sub-likelihood objects are chosen with probability depending on the variance of the corresponding McLE. A penalized version of our method generates sparse likelihoods with a relatively small number of components when the data complexity is intense. Our algorithms are illustrated through numerical examples on simulated data as well as real genotype single nucleotide polymorphism (SNP) data from a case–control study.