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estimation region相关的网络例句

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Therefore, in this article,we first discussed the compound negative binomial, it able to simultaneously fitthe claim size and the claim number. At the same time, we studied the sum of themutually independent compound negative binomial and the sum of the mutuallyindependent compound negative binomial with discount factor and the sum of themaximal compound negative binomial; Next, for the claim data to be better fitted, this article has establishedthe superposition distributed model about the weighted average of the discreterandom and has discussed the estimation method of the parameters of thesuperposition distribution; Once more, we in detail elaborated the NCD which based on the claim numberand the NCD which based on the claim size and the claim number. Besides, weestablished an applied NCD model which also based on the claim size and the claimnumber; At last, we introduced the rewards and punishment coefficient of automobiletravel region into the applied NCD model, availably consummated the present NCD.

因此,本文首先讨论了能拟合索赔额大小和索赔次数的复合负二项分布,并对相互独立的复合负二项分布的和、带有折现因子的相互独立的复合负二项分布的和以及最大复合负二项分布进行了研究;其次,为了更好地拟合索赔数据,本文建立了离散随机变量加权平均的叠加分布模型,并讨论了其中的权数的估计方法;再次,本文详细阐述了基于索赔次数的无赔款优待系统、考虑索赔次数和索赔额大小的无赔款优待系统以及应用型的同时考虑索赔次数和索赔大小的无赔款优待系统;最后,本文在无赔款优待系统中引入了汽车行驶区域奖惩系数,对现行的无赔款优待系统进行了完善。

According to the criterion of minimizing the sum of the squares of the states posterior confidence region widths, we get the value of the updated Biscay distribution, then get the states interval estimation.

该方法结合了区间运算和Kalman滤波方法,分别根据状态和观测方程得到的预测值的Biscay分布,融合得到估计的Biscay分布值,进而得到状态的区间估计。

Finally, based on the motion estimation and modifying region, a combined moving region segmentation and tracking algorithm has been proposed.

最后,将视频运动信息的检测与分析成功地应用于对象分割,提出了一种基于运动估计和区域修正的运动对象分割与跟踪联合算法。

We also discussed the estimation of stability region of discrete singular systems with delay, and presented several region estimaitons for uniform stabiity and asymptotic stability, respectively.

我们还研究了离散滞后广义系统稳定域的定量估计,分别给出了系统一致稳定区域和渐进稳定区域的估计公式。

For a general system, our Lyapunov function-based method can give a low conservative estimation for the region of attraction, moreover, a much more large region of attraction by using Matlab/LMI optimization.

对一般系统,我们用基于Lyapunov函数的方法给出保守性尽量低的吸引域的估计算法,再配合基于Matlab线性矩阵不等式优化方法去综合控制器,以便得到尽量大的吸引域。

The shape information of surveillance objects is represented as macro-block coordinates. The temporal redundancy is eliminated by motion estimation within the surveillance region in the reference frame. Since the number of luminance levels is reduced due to enhancement process, the residual error of motion estimation is compressed with lossless entropy coding.

监控对象的形状信息表示为宏块坐标;监控对象之间的时间冗余通过运动估计来消除,但搜索范围限制在前一帧监控对象区域之内;由于增强处理使得监控区域的灰度级减少了,所以对运动估计的残差进行无损熵编码。

Fist, comprehensive analysis on the current study on data processing is made, and characters of Newton methods about nonlinear surveying and mapping data processing are discussed, and then new solutions to parameters estimate with multi-sources, multi-types, multi-dimension, multi-precision bynonlinear least square are presented such as PSB algorithm, digital continuation and generalized digital continuation algorithm, cone model method, tensor analysis method, GCMA(mixed algorithm of gradient method and conjugate gradient method), combining algorithm based on Newton method and gradient method and confidence region and so on, and a new fast difference iterative algorithm is proposed towards parameters estimation containing random parameters in nonlinear models, and a new solutions to nonlinear least squares surveying and mapping adjustment by parameters estimation both considering the random and nonrandom parameters is presented after studying on nonlinear data processing in deformation monitoring, and at last primary analysis on error propagation of spatial data is made and approximate error propagation formula and error analysis formula to length and area are proposed.

首先比较全面分析了目前测量数据处理理论的研究现状,讨论了牛顿类方法在处理非线性模型参数估计方面的特点,研究并给出了求解多源、多类型、多维、多精度、非线性最小二乘测量平差参数估计的若干种新方法,这包括PSB算法、数值延拓及其广义数值延拓算法、锥模型法、张量分析法、基于最速下降法和牛顿法的组合算法、基于最速下降

Based on physical characteristics, existing stochastic estimation methods of equipment sensitivity to voltage sag use subjective probability models to express the probability distribution of voltage tolerance curve of equipment in the uncertain region. But the parameter estimation needs vast sample data. These methods may result in man-made errors.

现有负荷敏感度随机估计法以电压暂降的物理特征为基础,用主观概率模型描述负荷电压耐受曲线(voltage tolerance curve,VTC)的随机分布规律,所需样本量大,在实际中难以实现且可能引入主观误差。

Univariate indices are not adequate for assessing the capability of processes with multiple characteristics. Among all developed multivariate process capabilities, index has the most accurate estimation of Cpm. takes into account both the ratio of the tolerance region to the process region and the probability of nonconforming product. However, the exact probability distribution of is too complicated to be derived. Consequently, the related hypothesis testing and confidence interval cannot be developed.

在众多的指标当中,单变量制程能力指标的发展已趋成熟,而多品质特性的制程能力指标还在持续的发展中,其中多品质特性制程能力指标是所有多品质制程能力指标中估计值最准确的指标,但在利用多品质制程能力指标Cpm来评选两个制程的制程能力时,由於指标之机率分配过於复杂,迄今未能推导出其相关的统计假说检定或信赖区间,因此无法发展出检定两个制程能力指标Cpm是否有差异的统计检定程序。

First,owing to the wide range of use of WeibuU distribution,we propose the Weibull MTD model,which has improved the MTD model based on Gaussian distribution in some thick-tailed cases;Second,some stationarity properties of the model are discussed;After that,we apply the standard EM algorithm to mixture models to get the estimation equations and then expose bootstrap method to calculate confidence region of the parameters;Finally,some simulations and an example are analyzed in detail,which show that when analyzing nonstandard date,such as financial date,insurance date and so on,it is more proper for estimation to use the Weibull MTD model than the Gaussian MTD model.

首先给出基于Weibull分布的混合转移分布模型的一阶与k阶平稳的充分必要条件;接着,应用EM算法,得到了参数的极大似然估计及它们的标准误差;然后,应用Bootstrap方法,得到了参数的置信区间;最后,通过模拟与实例分析,说明该模型在分析来自金融、保险等某些厚尾分布的数据时,在参数估计方面的表现优于高斯混合转移分布模型。

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