1)M-estimationM估计
1.Research of Adaptively H_∞ Filter Based on M-estimation;基于M估计的H_∞自适应滤波技术研究
2.Parameter Identification Based on M-estimation and Iterative Algorithm in the Case of Non-Gaussian Noise;非高斯噪声下系统参数M估计及其递推算法
3.Application of M-estimation in medical images registrationM估计在医学图像配准中的应用
英文短句/例句
1.Linear strong representative of M-estimators in semiparametric regression models;半参数回归模型M估计的线性强表示
2.Trust Region Method of Implicit Nonlinear Model s M-estimate and Statisticians Property;隐非线性M估计的信赖域法及其统计性质
3.Strong Consistency of M-Estimator in Linear Model for Dependent Samples;相依样本线性模型参数M估计的强相合性
4.The Rate of Strong Convergence of M-estimate in Linear Model for NA Errors;NA误差下线性模型参数M估计的强收敛速度
5.Strong Convergence of M Estimator in Linear Model;再论线性模型参数M估计的强收敛速度
6.Restricted General Gauss-Markoff Estimation Based on Linear Transformations of the Dependent Variable;基于应变数线性变换的约束广义G-M估计(英文)
7.M-estimator based support vector machine and its application基于M估计器的支持向量机算法及其应用
8.APPROXIMATION TO THE DISTRIBUTION OF M-ESTIMATES IN RANKED-SET SAMPLING BY RANDOMLY WEIGHTED BOOTSTRAP序集抽样中M估计分布的随机加权逼近
9.Asymptotic Properties of M Estimator in Linear Model under Stochastic Adapted Errors随机适应误差下线性模型参数M估计的渐近性质
10.Method for Global Motion Estimation Based on Robust M-estimator基于鲁棒M-估计器的全局运动估计方法
11.Local M-Estimation for Varying-Coefficient Partially Linear Model;变系数部分线性模型的局部M-估计
12.Ambient Air-determination of Sulfur Dioxide with M-estimation;M—估计用于环境空气二氧化硫的测定
13.Channel Estimation in OFDM Systems Based on Mismatched-Sequence基于m序列的判决反馈OFDM信道估计
14.Variable bandwidth and one step local M-estimation of varying coefficient models变系数模型变窗宽和一步局部M-估计
15.Robust Power Harmonic Analysis Method Based on M-estimation基于M-估计的电力谐波稳健分析方法
16.An Improved Robust Method of M-Estimators一种改进的M-Estimators基础矩阵鲁棒估计法
17.Estimation of Value-at-risk Using PGARCH-M Model利用PGARCH-M模型估计风险值
18.The Robust M-estimation of Semi-varying Coefficient-Models and the Random Constrained Estimation with Co-relational Errors;半变系数模型的M-估计与误差相关下的随机约束估计
相关短句/例句
M-EstimateM估计
1.An Algorithm of Multi-Sensors Weighted Fusion Based on M-Estimate Applied in Single-Point Positioning;基于M估计的多传感器加权融合法在单点定位中的应用
2.Multi-Sensor Data Robust Weighted Fusion Algorithm Simulation Based on M-estimate;基于M估计的多传感器数据稳健加权融合算法仿真
3.M-estimate Based Kalman Filter with Immunity to Outliers;基于M估计的抗野值卡尔曼滤波方法
3)M estimatorM估计
1.M estimator in linear model for -mixing samples is disscussed.研究了混合样本线性模型中的M估计,在较弱的矩条件下,获得了M估计是强相合估计的充分条件。
2.The strong consistency of M estimator of regression parameter in linear model is established, under some suitable sufficient conditions which improves the relevant result of M estimator in linear model.本文研究线性模型中回归参数M估计的强相合性,给出一些较弱的充分条件。
3.In this paper, it is disscused the strong consistency of M estimator of regression parametric in linear model for-mixing samples.研究了混合样本线性模型中回归参数M估计的强相合性,在较弱的矩条件下,获得了M估计是强相合的充分条件,实质性地改进和推广了文[1]定理3。
4)M-estimatorM估计
1.Retrieving Method of Differential Optical Absorption Spectroscopy Based on M-estimator Robust Regression基于稳健回归M估计的差分吸收光谱反演方法(英文)
2.Under conditions that the innovations have a finite 12th moment,which allows the model to have a unit root,we show that the quasi-maximum likelihood estimator which uses the lognormal distribution as the likelihood is locally consistent and asymptotically normal by the properties of the M-estimator and functional central limit theorem for martingale.在新息序列具有有限的12阶矩条件下,利用M估计的大样本性质和鞅的泛函中心极限定理,允许模型包含一个单位根的情况下,证明了对数正态分布下的拟极大似然估计是局部相合和渐近正态的,并且对数正态分布的厚尾性也较好地解决了异常值问题。
3.The strong consistency of M-estimator of the regression coefficient in a nega- tively associated linear model under some mild condition is established,which greatly improves the corresponding results on the moment condition in literatures,respectively by Chen X R, Zhao L C(1996)and Yang Shanchao(2002).建立了随机误差为NA的线性模型中回归参数β_0的M估计的强相合性的充分条件。
5)M estimationM估计
1.For improving the accuracy, real-time property and reliability in GNSS real time single point navigation and positioning, three algorithms(Least squares estimation, M estimation and robust Kalman filter estimation) based on residual analysis for gross errors in real time observation were analyzed and compared.在利用卫星导航定位系统进行单点导航定位时,为了进一步提高导航定位的精度、实时性和可靠性,针对实时观测信息中存在粗差问题,基于残差分析理论,比较分析了最小二乘法、M估计、抗差滤波估计方法在GNSS单点导航定位中参数估计问题。
6)M-estimationM-估计
1.Although M-estimation as the object function can be used to solve the problem,its corresponding influence function is determined by the absolute value of gross error and it is a key problem to choose initial parameters.虽然以M-估计作为目标函数可以解决这个问题,但由于其对应的影响函数由残差绝对值决定,因此如何选择初始参数值成为一个关键问题。
2.This method is based on M-estimation.针对存在粗差或异常数据点时,最小二乘定位方法会产生定位错误的情况,本文提出了基于M-估计的稳健标靶球定位方法。
延伸阅读
Bayes估计量Bayes估计量Bayesian estimator Bayes估计量【Bayesi助始廿ma.件;D自狱.。眨..界..] 用BayeS方法(Bayesian aPProach)由观察值对一未知参数所作的估计.统计问题使用这样的方法时，一般都假定未知参数所0 gR“是一具有给定先验分布7r=武do)的随机变量，决策空间D与集合0重合.且损失L(0，d)表示变量0与估计d的偏离.因此，函数L勿，d)通常假定为有形式L勿，d)=a(e)又(口一d)，其中又是误差向量0一d的某个非负函数，若k二1，则常取又勿一d)={0一d}“(“>0).最有用且在数学上最方便的是平方损失函数L(口，d)=}‘一d1’.对这一损失函数，Bayes估计量(Ba卿决策函教(Bavesian dedsion function))占’二亡厂(x)定义为使最小总损失 !;‘p‘二·“，一，‘薯必，“一”‘·’2’〕口‘么，叮‘““，达到的函数，或与之等价，了是使最小条件损失 ，母‘E{[口一占(x)]2+“)达到的函数，由此推出，在平方损失函数的场合，B竹es估计量与后验均值占‘(x)=E勿{x)相等，而Bayesj双险(Bayes risk)为 。‘二，占‘)二E!D矿夕}x)]‘此处O(0}劝是后验分布的方差: o(口{x)二任{{口一E(0{x)12!，、}. 例设二=(x，，，二，戈)，这里x，，，二，x。为具正态分布N勿，。’)的独立同分布变量，护己知，而未知参数0有正态分布N扭，铲).因为当x给定时口的后验分布为正态N(拜。，T:一、其中 n又。2一十“下一2 灿。二一—，，。一二n口‘一奋了一_ n口一汁~下且万=(x，十一+凡)/。，可知在平方损失函数{分一引’之下，Bayes估计量为占’(x)=线，而Bayes风险则为《二犷六伽铲十护).AH川畔即撰[补注]
