For arbitrary conditional estimable function,the linear conditional minimax estimator under a given quadratic loss function is defined and the unique linear conditional minimax estimator is obtained.
对任一条件可估函数 ,给出了二次损失下线性条件 Minimax估计的定义 ,并得到了唯一的线性条件Minim ax估计 。
For arbitrary estimable function,the unique linear minimax estimator under a given matrix loss function is obtained in the class of homogeneous linear estimators.
考虑方差分量模型,对任一可估函数,在二次损失下得到了线性可估函数在齐次估计类中的唯一的线性M in-im ax估计。
Let Y be a random n-vector with mean Xβ and covariance matrix σ2V, and S be a linear estimable function, where X, Sβ and V ≥ 0 are known matrics, ∈ Rp and σ ≥ 0 are unknown parameters.
设Y是具有均值Xβ和协方差阵σ~2V的n维随机向量,Sβ是线性可估函数,这里X,S和V≥0是已知矩阵,β∈R~p和σ~2>0是未知参数。
Reinforcement learning function approximation algorithm based on linear average;
基于线性平均的强化学习函数估计算法
Blind source separation based on optimally selected estimating functions;
基于选优估计函数的盲信号分离
Secondly, based on the semiparametric theory, an estimating function is constructed and the corresponding learning algorithms are proposed.
基于此,采用半参数统计方法构造超定盲信号分离的估计函数,给出相应的学习算法;理论证明了该算法具有等变化性和分离矩阵的非奇异特性,并借助于源信号数目未知且动态变化的计算机仿真验证了其有效性。
Firstly, the semiparametric statistical approach is introduced into the BSS, and an estimating function for the semiparametric statistical approach in BSS is proposed, from which a learning rule is obtained.
将半参数统计模型引入源信号个数未知的盲分离中,给出了源信号个数(其值n不大于观测信号的个数m)未知,混合矩阵列满秩时,盲分离半参数统计模型的估计函数,得到了由此估计函数给出的半参数统计学习算法。
The approach can ensure the minimum actual risk of denoised signals in the view of function estimation,overcoming the drawbacks of application of traditional wavelet-denoising approaches.
根据统计学习的结构风险最小化原则和VC维理论,给出一种改进的基于VC维的小波消噪方法,使消噪后信号在函数估计意义下具有最小的实际风险,克服了传统的小波信号消噪方法的应用缺陷。
THE LINEAR MINLMAX ESTIMATE OF ESTIMABLE FUNCTION IN A GENERAL NORMAL LINEAR MODEL;
一般正态线性模型中可估函数的线性Minimax估计
On Minimax Estimators of Estimable Functions in Normal Linear Experiments;
正态线性模型中可估函数的极小极大估计(英文)
On Minimax Estimators of Estimable Funetions in Normal Linear Experiments;
正态线性试验中可估函数的最小最大估计
The Minimax Admissibility of the Estimable Function and the Linear Prediction under Quadratic Loss Fuction;
二次损失下可估函数与线性预测的Minimax可容许性
The Minimax Adimissibility of the Linear Estimator in the Gauss-markov Model under the Quadratic Function;
二次损失下一般Gauss-Markov模型中可估函数的Minimax可容许性
The admissible linear estimates of the mean matrix on the matrix-normal distribution
矩阵正态分布均值矩阵的可估函数的线性估计在线性估计类中的泛容许性
assessment of additive utility function
可加效用函数的评估
Mean Value on Some Arithmetical Functions and Solvability of the Functional Equations;
一些数论函数的均值估计及一类函数方程的可解性
Bayesina Estimation of Geometric Distribution Parameter under Entropy Loss Function;
熵损失函数下几何分布可靠度的Bayes估计
Henstock integrable function and staircase function;
Henstock可积函数与阶梯函数
Parameter Estimation of Archimedean Copula
Archimedean Copula函数的参数估计
Admissibility of Parameter Estimators in Linear Model under Vector Loss Function;
向量损失函数下线性模型中参数估计的可容许性
countably subadditive function
可数次可加性的函数
All Admissible Estimators of the Function of Mean Matrix in Multivariate Linear Models
多元线性模型中均值矩阵的函数的所有可容许线性估计
Estimates for Nonparametric Regression Functions with Missing Data
缺失数据情形非参数回归函数的估计
Nonparametric estimation of the production function with time-varying elasticity coefficients
时变弹性系数生产函数的非参数估计
Piecewise Function,Integrability and Existence of Primitive Function
分段函数、函数的可积性与原函数存在性
Research on Angular Deveation Estimation of Modulus Function of Grotzsch Domain Function;
关于Grotzsch区域函数的模函数的角偏差估计研究
