Geometry of skew information-based quantum coherence

We study the skew information-based coherence of quantum states and derive explicit formulas for Werner states and isotropic states in a set of autotensors of mutually unbiased bases (MUBs). We also give surfaces of skew information-based coherence for Bell-diagonal states and a special class of X states in both computational basis and in MUBs. Moreover, we depict the surfaces of the skew information-based coherence for Bell-diagonal states under various types of local nondissipative quantum channels. The results show similar as well as different features compared with relative entropy of coherence and l₁ norm of coherence.     

一类线性模型中Bayes估计的优良性

本文研究了一类线性模型中参数的Bayes线性无偏估计的优良性.利用矩阵论的相关知识,分别在平衡损失准则和均方误差阵准则下,得到了Bayes线性无偏估计优于广义最小二乘估计的条件.     

观测站非机动情况下的渐进无偏目标跟踪算法

针对无源目标跟踪中将非线性测量方程伪线性化带来有偏估计的缺点,提出了一种基于方位和径向速度联合估计的渐进无偏目标跟踪算法。该算法通过对方位-径向速度组成的伪线性测量方程的噪声协方差矩阵进行约束,使其均方误差达到最小,得到目标状态向量的最小二乘无偏解。仿真和实验结果表明,在观测站非机动情况下,算法能够渐进无偏地收敛到真值,收敛后的距离跟踪误差在5%以内。     

单总体方差检验中一致最大势检验（UMPUT）临界值的研究

方差检验问题在实际生活中应用广泛,对于同一假设可能有不同的检验方法,这就涉及到检验方法的优劣性问题,本文就正态总体对单总体方差检验作了一些研究．对单总体问题,考虑H0：σ^2=σ0^2vsH1：σ^2≠σ0^2,通常采用的χ^2检验并不是无偏的,给出了无偏检验的临界值．将该无偏检验与常用的χ^2检验就势函数进行比较,指出当样本量n不大时二者是有差别的．     

ADAPTIVE NONLINEAR FEEDBACK CONTROL OF CHAOTIC SYSTEMS BASED ON REDUCED PARAMETER QUADRATIC PREDICTORS

An adaptive nonlinear feedback-control method is proposed to control continuous-time chaotic dynamical systems, where the adaptive nonlinear controller acts on only one-dimensional error signal between the desired state and the observed chaotic state of a system. The reduced parameter adaptive quadratic predictor used in adaptive feedback cancellation of the nonlinear terms can control the system to any desired state. Computer simulation results on the Lorenz system are shown to demonstrate the effectiveness of this feedback-control method.     

在单参数双边截断型分布族中参数估计的一种新的渐近效率

为了克服在单参数双边截断型分布族中不能消除参数估计中在Ｂａｈａｒｄｕｒ意义下的超有效病态现象,本文提出了一种新的渐近效率．根据这种效率的定义,对一般 的参数函数,构造了适用的渐近中位无偏的渐近有效估计量．作为全文的理论基础,我们发现了渐近中位无偏估计的最优收敛速度．     

最小范数二次无偏估计与最小方差二次无偏估计的关系

本文给出了当V0 ≥ 0时 ,c′σ2 在混合模型M =( y ,Xβ ,Uξ,σ20 V0 )下的最小范数二次无偏估计的表达式及其证明 ;得到了当 y服从正态分布时 ,c′σ2 的最小范数二次无偏估计与其最小方差二次无偏估计之间的关系。     

A Robust Solution to Variational Importance Sampling of Minimum Variance

Importance sampling is a Monte Carlo method where samples are obtained from an alternative proposal distribution. This can be used to focus the sampling process in the relevant parts of space, thus reducing the variance. Selecting the proposal that leads to the minimum variance can be formulated as an optimization problem and solved, for instance, by the use of a variational approach. Variational inference selects, from a given family, the distribution which minimizes the divergence to the distribution of interest. The Rényi projection of order 2 leads to the importance sampling estimator of minimum variance, but its computation is very costly. In this study with discrete distributions that factorize over probabilistic graphical models, we propose and evaluate an approximate projection method onto fully factored distributions. As a result of our evaluation it becomes apparent that a proposal distribution mixing the information projection with the approximate Rényi projection of order 2 could be interesting from a practical perspective.     

Asymptotically minimum variance unbiased estimation for a class of power series distributions

Summary The problem of finding an asymptotically minimum variance unbiased estimator (A.M.V.U.E.) for the parameter of certain truncated power series distributions, is discussed, when the generating function of their coefficients are i) polynomials of binomial type ii) generalized ascending factorials iii) polynomials with coefficients the well known Eulerian numbers.     

Second order asymptotic optimality of estimators for a density with finite cusps

We consider i.i.d. samples from a continuous density with finite cusps. Then we obtain the bound for the second order asymptotic distribution of all asymptotically median unbiased estimators. Further we get the second order asymptotic distribution of a bias-adjusted maximum likelihood estimator, and we see that it is not generally second order asymptotically efficient.