用单个矢量水听器基于DEMON线谱的双目标分辨

不同目标的解调线谱频率是不同的。本文提出了一种在DEMON谱域内对双目标测向的方法,它具有较好的抗相干及非相干干扰能力,表明单个矢量水听器也能分辨多目标。文中叙述了利用DEMON线谱测向的原理,给出了轴频提取及双目标DEMON线谱分离的方法,对DEMON线谱区分双目标的能力做了仿真分析。     

Optimal uncertainty size in distributionally robust inverse covariance estimation

In a recent paper, Nguyen et al. (2018) built a distributionally robust estimator for the precision matrix of the Gaussian distribution. The distributional uncertainty size is a key ingredient in the construction of this estimator. We develop a statistical theory which shows how to optimally choose the uncertainty size to minimize the associated Stein loss. Surprisingly, rather than the expected canonical square-root scaling rate, the optimal uncertainty size scales linearly with the sample size.     

Weibull分布步进应力加速寿命试验的Bayes估计

本文研究了定时和定数截尾情形CE模型下Weibull分布场合步进应力加速寿命试验的Bayes估计.利用加速系数和加速方程将各种加速应力水平下的尺度参数换算为正常应力水平下的尺度参数,从而获得含正常应力下尺度参数的似然函数.在参数先验的选取时,尺度参数和加速系数分别取共轭先验和无信息先验,当形状参数m<1和m>1时分别取Beta分布和Gamma分布作为其先验.在平方损失下,利用Gibbs抽样和切片抽样给出了该模型参数的Bayes估计.最后,通过Monte Carlo模拟表明该Bayes估计是有效的.     

关于James-Stein估计的小样本性质

首先给出了Jam es-S te in估计优于岭估计的充分条件,随后在P itm an准则下给出了Jam es-S te in估计优于最小二乘估计的简短证明.     

Parametric estimation of discretely sampled Gamma-OU processes

The stationary Gamma-OU processes are recommended to be the volatility of the financial assets. A parametric estimation for the Gamma-OU processes based on the discrete observations is considered in this paper. The estimator of an intensity parameter A and its convergence result are given, and the simulations show that the estimation is quite accurate. Assuming that the parameter A is estimated, the maximum likelihood estimation of shape parameter c and scale parameter a, whose likelihood function is not explicitly computable, is considered. By means of the Gaver-Stehfest algorithm, we construct an explicit sequence of approximations to the likelihood function and show that it converges the true (but unkown) one. Maximizing the sequence results in an estimator that converges to the true maximum likelihood estimator and the approximation shares the asymptotic properties of the true maximum likelihood estimator. Some simulation experiments reveal that this method is still quite accurate in most of rational situations for the background of volatility.     

非寿险分类费率模型及其参数估计

在非寿险分类费率厘定中,存在各种模型可供选择,如加法模型、乘法模型、混合模型和广义线性模型等,而在这些模型的参数估计中,还存在各种可供选择的估计方法,如最小二乘法、极大似然法、最小x2法、直接法和边际总和法等。这些模型和参数估计方法散见于各种精算学文献中,本文对这些模型和参数估计方法进行了系统的比较和分析,并揭示了它们之间存在的一些等价关系。     

半变系数模型PLS估计的渐近正态性

半变系数模型在统计建模中具有重要的应用．最近几年，人们提出了许多方法来估计其常系数和函数系数，但是估计的渐近性质还没有被系统的研究．本文介绍了半变系数模型的PLS估计，在Fan和Huang对常系数渐近性质研究的基础上，给出了函数系数的渐近正态性。     

非时齐扩散模型中时变参数的局部线性估计

本文主要研究非时齐扩散模型中时变的漂移参数和扩散参数的局部线性估计。基于非时齐扩散模型的离散观测样本,首先得到了漂移参数的局部线性估计及其标准误差。然后,考虑到扩散参数的非负性,本文利用局部对数线性拟合的方法得到了扩散参数的核函数加权估计,并讨论了扩散项估计的渐近偏差、渐近方差和渐近正态性。最后,通过模拟研究表明所得局部估计有很好的拟合效果。     

适用于二级与多级评分项目组成的混合测试的加权最大似然潜在特质估计（英文）

提出适用于二级与多级评分项目组成的混合测试的加权最大似然潜在特质估计.使用N-R算法获得WML估计,并给出相关方程的详细推导.为探讨WML的性能,进行了模拟研究,所得到的结果表明,WML的估计比最大似然估计（MLE）具有更好的性能.最后,基于一个实际例子对该方法进行了实证研究.     

Analyzing continuous state space failure time processes: Two further results*

In a recent paper, Petersen (1988) considered a continuous state space failure time process. The central result provided in that paper was that the destination‐specific rate of transition of the process can be specified in two steps. First, one specifies the overall rate at which a change occurs. Then, one specifies the probability density function of the destination state, given that a transition occurred. This two‐step property was used in deriving the likelihood of the data and was exploited for purposes of estimation. The overall rate of transition can be estimated from the data on durations between changes in the dependent variable. The density for the new value of the dependent variable, given a change, can be estimated from the data on the values of the dependent variable after the change.

This paper extends these results in two ways. First, it is shown that one can derive the likelihood of the process directly from the destination‐specific rate of transition, without going through its decomposition into the overall rate times the density of the destination state, given a transition. Once the likelihood is derived, estimation is comparatively straightforward. Second, it is shown how one can derive, at each point in time, a more standard regression function for the continuous dependent variable, where its value is expressed in terms of its conditional mean plus an error term.