Harnack inequality and derivative formula for SDE driven by fractional Brownian motion

In the paper, Harnack inequality and derivative formula are established for stochastic differential equation driven by fractional Brownian motion with Hurst parameter H < 1/2. As applications, strong Feller property, log-Harnack inequality and entropy-cost inequality are given.     

Large deviation principle for diffusion processes under a sublinear expectation

We represent the exponential moment of the Brownian functionals under a nonlinear expectation according to the solution to a backward stochastic differential equation.As an application,we establish a large deviation principle of the Freidlin and Wentzell type under the corresponding nonlinear probability for diffusion processes with a small diffusion coefficient.     

不完全信息下型截尾Weibull分布参数的极大似然估计及贝叶斯估计

给出了不完全信息下 型截尾weibull分布参数的极大似然估计、无信息先验Bayes估计及多层Bayes估计,并指出针对一些具体模型还可以通过随机模拟来比较其估计精度.     

不完全信息下Ⅱ型截尾Weibull分布参数的极大似然估计及贝叶斯估计

给出了不完全信息下Ⅱ型截尾weibull分布参数的极大似然估计、无信息先验Bayes估计及多层Bayes估计,并指出针对一些具体模型还可以通过随机模拟来比较其估计精度.     

商业银行操作风险计量模型的Bayes估计

对于商业银行来讲,一个很重要的问题是损失数据缺乏,而损失数据缺乏会影响模型参数的估计,用Bayes估计解决了这一问题.Bayes估计的方法利用商业银行专家提供的意见确定先验分布,能够有效地解决损失数据缺乏的问题.实证分析的结果表明,Bayes估计与极大似然估计的结果.不考虑存在着一定的差距.不考虑各部分风险之间的相关性,基于Bayes估计与极大似然估计时VaR与ES的大部分结果相差不大.     

带有不完全信息的随机截尾广义线性模型的中偏差

本文介绍了随机截尾的带有不完全信息的广义线性模型,并在一定条件下运用Taylor渐近展开方法得到了此模型的极大似然估计的中偏差.     

对称熵损失下成功概率p的E-Bayes估计

研究对称熵损失下成功概率p的Bayes估计和E-Bayes估计,证明了前者的存在性及唯一性.模拟结果表明E-Bayes估计优于极大似然估计和Bayes估计.并将E-Bayes方法应用在证券投资预测之中,预测效果较好.     

多重Ⅱ型删失数据的近似似然函数及应用

多重Ⅱ型删失数据是一种很常见的数据删失类型，处理起来也非常困难，本文获得了多重Ⅱ型删失数据的一种近似似然函数，并证明了在大样本场合下，这种近似与似然函数是等价的。基于该近似似然函数，求得了参数的近似极大似然估计与近似Bayes估计，并讨论似极大似然估计的性质。     

对称熵损失下成功概率P的E—Bayes估计

研究对称熵损失下成功概率P的Bayes估计和E—Bayes估计,证明了前者的存在性及唯一性．模拟结果表明E-Bayes估计优于极大似然估计和Bayes估计．并将E—Bayes方法应用在证券投资预测之中,预测效果较好．     

敏感问题调查设计以及估计

对于多项选择敏感问题抽样调查,为了避开以往使用随机化调查时使用随机化装置带来的不便,提出一种间接问卷调查策略.结合问卷设计对个体隐私的保护度,对辅助信息的选择提出了的建议,并得到了敏感属性比例的极大似然估计和Bayes估计.数据模拟的结果显示,相比于极大似然估计,小样本时Bayes估计便有较高的精度.随着样本量的增加,两种估计结果趋于一致.提出的间接问卷调查策略,相比于以往的随机化调查技术,具有方便、省时等特点,可在实践中应用.     

Bayes估计的中偏差下界

在某种正则条件下，对Bayes估计尾概率收敛速度问题进行了讨论。利用似然理论方法得到了Bayes估计的中偏差下界，从而改善了Bahadur型的收敛结果。     

On large deviation expansion of distribution of maximum likelihood estimator and its application in large sample estimation

For estimating an unknown parameter , the likelihood principle yields the maximum likelihood estimator. It is often favoured especially by the applied statistician, for its good properties in the large sample case. In this paper, a large deviation expansion for the distribution of the maximum likelihood estimator is obtained. The asymptotic expansion provides a useful tool to approximate the tail probability of the maximum likelihood estimator and to make statistical inference. Theoretical and numerical examples are given. Numerical results show that the large deviation approximation performs much better than the classical normal approximation.This work is supported in part by the Natural Science and Engineering Research Council of Canada under grant NSERC A-9216.This author is also partially supported by the National Science Foundation of China.     

Deviation inequalities,moderate deviation principles for certain Gaussian functionals,and their applications in parameter estimation

In this article, we study the deviation inequalities, moderate deviation principle (MDP) and Berry–Esseen bounds for certain Gaussian functionals arising from the Ornstein-Uhlenbeck process without tears. As an application, several asymptotic properties for the minimum distance estimator are obtained. The main methods include the MDP and deviation inequality for multiple Wiener–Itô integrals.     

带有辅助信息的ROC曲线两种估计的比较

在医学研究中,常常使用受试者操作特性曲线(ROC)曲线来研究两样本的比较问题。Lloyd构造了ROC曲线的核平滑估计,并给出了其渐近偏差以及渐近标准差。此外,当还可以获悉某一处理组上的辅助信息时,Zhou,Zhou & Ma利用经验似然的方法构造了ROC曲线的核平滑经验似然估计。本文利用"亏量"这个概念比较了带有辅助信息的情况下,对核平滑经验似然估计与完全经验似然估计进行了比较。并给出了核平滑经验似然估计优于完全经验似然估计的结论,并且随着样本容量的增大,该亏量也是无限增大的。     

截断与删失数据的极大似然估计的中偏差

本文研究了截断与删失模型,运用Taylor渐近展开方法,得到模型的极大似然估计的中偏差,比渐近正态性结果更加精细.     

柯西分布的参数估计

研究了柯西分布的参数估计问题,给出了位置参数的最小一乘估计和尺度参数的低阶矩估计.证明了柯西分布位置参数的最小一乘估计具有渐近无偏性与强相合性;尺度参数的低阶矩估计具有强相合性.     

A Comparison of Restricted and Unrestricted Estimators in Estimating Linear Functions of Ordered Scale Parameters of Two Gamma Distributions

The problem of estimating linear functions of ordered scale parameters of two Gamma distributions is considered. A necessary and sufficient condition on the ratio of two coefficients is given for the maximum likelihood estimator (MLE) to dominate the crude unbiased estimator (UE) in terms of mean square error. A modified MLE which satisfies the restriction is also suggested, and a necessary and sufficient condition is also given for it to dominate the admissible estimator based solely on one sample. The estimation of linear functions of variances in two sample problem and also of variance components in a one-way random effect model is mentioned.     

指数分布抽样基本定理及在三参数一般指数分布参数估计中的应用

讨论三参数一般指数分布的参数估计,首先讨论了三参数一般指数分布参数的最大似然估计的求解问题,当其中参数α=1时,应用指数分布抽样基本定理,得到了三参数一般指数分布其它参数的一致最小方差无偏估计;并且由此给出求解三参数一般指数分布参数最大似然估计的迭代方法,得到了三参数一般指数分布参数最大似然估计的近似值,给出了模拟结果以说明迭代方法的收敛性;并以相关文献的观察数据作为样本,得到了三参数一般指数分布的参数估计,从而说明了迭代方法的有效性.     

Some closed form robust moment‐based estimators for the MEM(1,1)

In this paper, we extend the closed form moment estimator (ordinary MCFE) for the autoregressive conditional duration model given by Lu et al (2016) and propose some closed form robust moment‐based estimators for the multiplicative error model to deal with the additive and innovational outliers. The robustification of the closed form estimator is done by replacing the sample mean and sample autocorrelation with some robust estimators. These estimators are more robust than the quasi‐maximum likelihood estimator (QMLE) often used to estimate this model, and they are easy to implement and do not require the use of any numerical optimization procedure and the choice of initial value. The performance of our proposal in estimating the parameters and forecasting conditional mean μ_t of the MEM(1,1) process is compared with the proposals existing in the literature via Monte Carlo experiments, and the results of these experiments show that our proposal outperforms the ordinary MCFE, QMLE, and least absolute deviation estimator in the presence of outliers in general. Finally, we fit the price durations of IBM stock with the robust closed form estimators and the benchmarks and analyze their performances in estimating model parameters and forecasting the irregularly spaced intraday Value at Risk.