函数系数部分线性模型的B样条估计

函数系数部分线性模型是一个比较广泛的模型,其常数项函数和系数函数具有不同的自变量．这给模型的估计带来了不小的挑战。利用B样条方法同时给出该模型的常数项函数和系数函数的估计,给出了估计的相合性、渐近正态性以及收敛速度,且该收敛速度达到了非参数最优收敛速度;最后,模拟说明了B样条方法对该模型的估计是有效的。     

半变系数模型的估计及性质

半变系数模型在统计中具有重要的应用,本文时该模型的常数系数,采用局部多项式估计方法和平均方法,给出了它的估计,对于函数系数的估计通过应用常数系数的估计,采用局部多项式估计方法给出其估计,并给出估计的渐近正态性和证明．     

一类新的变系数模型的积分估计

该文从实际出发给出了一类实用范围较广的变系数模型,它们的系数函数的自变量(也称光滑变量)不完全一致.首先,使用局部线性方法给出模型的系数函数的初始估计;然后使用积分方法,给出它们的积分估计;进一步,研究这些积分估计的渐近正态性.模拟结果说明该估计方法的有效性.     

Entropy损失函数下两参数指数威布尔分布尺度参数的Bayes估计及其性质

在Entropy损失函数下,利用构造多层先验分布的方法求出了指数威布尔分布参数的多层Bayes估计,然后根据经验Bayes估计的思想,利用密度函数的核估计方法,构造了参数的经验Bayes估计并证明了该估计的渐进最优性和可容许性,最后运用随机模拟,将其与平方损失函数下的Bayes估计以及极大似然估计(MLE)进行了比较,结果表明:Entropy损失下的Bayes估计较后两种估计好。     

熵损失函数下两参数指数-威布尔分布形状参数的可容许性估计

在熵损失函数下,得到了两参数指数-威布尔分布形状参数的Bayes估计和可容许估计,并讨论了一类(cT+d)-1形式估计的可容许性和不可容许性.     

二次损失下一般Gauss-Markov模型中可估函数的Minimax可容许性

对一般Gauss-Markov模型在齐次线性估计类中研究了Minimax可容许性,得出了线性Minimax估计在齐次线性估计类中可容许性的充分必要条件,接着对二次损失函数作了一些修改,采用类似齐次线性估计类中Minimax可容许性的研究方法,在非齐次线性估计类中研究了Minimax可容许性,得出了线性Minimax估计在非齐次线性估计类中可容许性的充分必要条件。     

一种对称损失下Rayleigh分布参数倒数的Bayes估计

文中研究了在一种对称损失函数下,Rayleigh分布参数倒数的Bayes估计及其可容许性,并给出了Bayes估计的置信下限和多层Bayes估计.     

熵损失函数下逆Rayleigh分布形状参数的估计

在熵损失函数下,得到了逆R ayleigh分布形状参数的Bayes估计和经验Bayes估计,并讨论了一类逆线性形式估计（cT＋d）-1的可容许性和不可容许性.     

对称损失下一类指数分布族的Bayes估计

在对称损失函数下,研究了一类指数分布族尺度参数的估计,并研究了它的的可容许性.得到了尺度参数的Bayes估计的一般形式,在共轭先验分布下得到了尺度参数的Bayes估计的精确形式.在此基础上,讨论了一类形式如cT+d估计量的可容许性和不可容许性.     

区间约束数据条件分位数估计的大样本性质

对一组区间约束数据进行研究,利用核函数方法构造出区间约束数据的条件分位数的估计,在一定的正则条件下证明了估计量的强弱相合性,渐近正态性,并构造了条件分位数的经验似然置信区间.     

一类指数分布族的参数估计问题

考虑了一类单参数指数族分布的估计问题,在熵损失下讨论了此类分布中未知参数的Bayes估计，给出了未知参数的一个无偏估计并验证了此估计量所具有的一些优良性质,最后在对称熵损失下得出了所讨论参数的Bayes估计。     

A noise cross PSD estimator for dual-microphone speech enhancement based on minimum statistics

Some two-microphone noise reduction techniques that work in the frequency domain exploit coherence function between two noisy signals. They have shown good results when noise signals on two sensors are uncorrelated, but their performance decreases with correlated noises. Coherence based methods can be improved when the cross power spectral density （CPSD） of correlated noise signals is available. In this paper, we propose a new method for estimation of the CPSD of the noise, which is based on the minimum tracking technique. Despite the fact that the proposed estimator does not need to implement a voice activity detector （VAD）, its performance is comparable to a CPSD estimator that uses an ideal VAD.     

回归函数的非参数分块Delta序列估计

在非参数回归模型中,本文提出一种回归函数的分块Delta序列估计方法,定义了回归函数的分块Delta序列估计,得到这种估计的渐近无偏性,均方收敛性和强性敛性.     

排序抽样的非参数估计

针对未知对称分布函数提出新的非参数估计量,并证明了此估计量一致优于文献[1]给出的估计量.     

Powell方法的Sargent改进算法的收敛性

本文指出文[1]的目标函数下降量估计和共轭性度量估计对于Powell方法的Sargent改进算法依然成立,从而在一致凸函数的条件下,该算法也有局部线性收敛性。     

Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering

A model-based estimator design and implementation is described in this paper to undertake combined estimation of vehicle states and tire-road friction coefficients. The estimator is designed based on a vehicle model with three degrees of freedom (3-DOF) and the dual extended Kalman filter (DEKF) technique is employed. Effectiveness of the estimation is examined and validated by comparing the outputs of the estimator with the responses of the vehicle model in CarSim in three typical road adhesion conditions (high-friction, low-friction, and joint-friction roads). Simulation results demonstrate that the DEKF estimator algorithm designed is able to obtain vehicle states (e.g., yaw rate and roll angle) as well as road friction coefficients with reasonable accuracy.     

SVM for density estimation and application to medical image segmentation

A method of medical image segmentation based on support vector machine （SVM） for density estimation is presented. We used this estimator to construct a prior model of the image intensity and curvature profile of the structure from training images. When segmenting a novel image similar to the training images, the technique of narrow level set method is used. The higher dimensional surface evolution metric is defined by the prior model instead of by energy minimization function. This method offers several advantages. First, SVM for density estimation is consistent and its solution is sparse. Second, compared to the traditional level set methods, this method incorporates shape information on the object to be segmented into the segmentation process. Segmentation results are demonstrated on synthetic images, MR images and ultrasonic images.     

Statistical Assessment of Estimated Transformations in Observed‐Score Equating

Equating methods make use of an appropriate transformation function to map the scores of one test form into the scale of another so that scores are comparable and can be used interchangeably. The equating literature shows that the ways of judging the success of an equating (i.e., the score transformation) might differ depending on the adopted framework. Rather than targeting different parts of the equating process and aiming to evaluate the process from different aspects, this article views the equating transformation as a standard statistical estimator and discusses how this estimator should be assessed in an equating framework. For the kernel equating framework, a numerical illustration shows the potentials of viewing the equating transformation as a statistical estimator as opposed to assessing it using equating‐specific criteria. A discussion on how this approach can be used to compare other equating estimators from different frameworks is also included.     

在平衡损失函数下的泛最小二乘估计

从平衡损失函数的角度研究线性回归模型的泛最小二乘估计.用L-曲线的方法得到了平衡参数的简单而又实用的公式,研究了泛最小二乘估计量的风险函数,并用一个实际应用说明了本文的结论.     

回归函数加权核估计的r阶矩收敛速度

分别在完全样本和删失样本下,当误差为独立或混合序列时,获得了非参数回归函数加权核估计的r阶矩收敛速度.