estimators的例句大全

In particular, we analyze the asymptotic theory of estimation and evaluate various efficient estimators.

特别地，我们分析了统计量的渐进理论，评估了统计量的各种有效性。

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Asymptotically strongly consistent estimators of τ are given and the hypothesis test and interval estimation are also discussed.

同时也讨论了τ的假设检验和区间估计。

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All k-Admissible Estimators of Regression Coefficients in Multivariate Normal Linear Models with Unknown Error Variance

具有未知误差方差的多元正态线性模型中回归系数的所有k-容许估计

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And specially, unifying steady state white noise estimators are also presented for time invariant systems.

特别对时不变系统提出了统一的稳态白噪声估值器。

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Estimators of Parameters in A Single Index Time Series Model

单指标时间序列模型参数估计

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The input white noise estimators and measurement white noise estimators, optimal and steady_state white noise estimators, fixed point, fixed lag and fixed interval white noise smoothers, and white noise innovation filter and Wiener filter are presented.

提出了输入白噪声估值器和观测白噪声估值器，最优和稳态白噪声估值器，固定点、固定滞后和固定区间白噪声平滑器，白噪声新息滤波器和Wiener滤波器。

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Bayesian Inference for the Loss and Risk Function of Parameter Estimators

参数估计的损失与风险的贝叶斯推断

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We discussed the effect produced by risk function on deciding rule between two parameter estimators, gave a probability rule for choosing estimator.

探讨了风险函数对评判参数估计优劣的影响，提出了衡量估计优劣的一个概率判定准则。

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Second, we obtain the bayes estimators of the loss function and risk function based on the conjugate prior distribution for the Weibull distribution, also discuss the conservative property and the rationality of these bayes estimators.

其次在共轭先验分布下给出了Weibull分布参数估计的损失函数和风险函数的贝叶斯估计，并讨论了它们的保守性和合理性。

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Abstract: For the normal distribution N(μ,σ 2), when σ 2 is known, the Bayesian estimators of loss function and risk function under different criterions are obtained, and the properties of these estimators are also studied.

本文给出了在方差已知的情况下，正态分布期望参数的估计的损失与风险在不同的先验分布下的贝叶斯估计，并研究了其性质。

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Firstly, under the conjugate prior distribution, we obtain the Bayes estimators of the loss function and risk function for Γ parameter estimator and normal variance estimator, also discuss the conservative property and the rationality of these Bayes estimators.

首先，在共轭先验分布下给出了Γ分布参数估计，正态分布方差估计的损失函数和风险函数的Bayes估计，并讨论了它们的保守性质和合理性。

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Under the conjugate prior distribution, the Bayes estimators of the loss function and risk function for the estimators of Parameter 1/ θ of Γ( r,θ) are investigated. Then the property and the rationality of the Bayes estimators are discussed.

在共轭先验分布下本文给出了Γ分布Γ（r，θ）中参数1/θ的估计的损失函数和风险函数不同的Bayes估计，并讨论了各种Bayes估计的性质，进而指出各种Bayes估计的合理性。

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In the estimation of Markov switch systems, the wellknown multiple model estimators ( MME) detect model switch based on model probability.

在Markov跳变系统的估计问题中，目前的多模型估计器采用模型概率检测模型切换；

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Li and Duan use maximum likelihood type criteria to get a consistent series of estimators of the direction.

文中，利用最大似然型的准则，找到了回归系数方向的相合估计。

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Convergency for Empirical Bayes Estimators of Parameters in Variance Component Model

方差分量模型参数经验Bayes估计收敛性

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oversampling the received signal generates cyclostationary statistics that are exploited to devise symbol rate estimators by maximizing in the cyclic domain a sun of modulus squares of cyclic correlation estimates.

对接收信号进行过采样，利用信号的循环平稳统计特性，在循环域对信号的循环相关估计的模平方之和进行最大化处理，从而导出本文描述的符号速率估计器。

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the binned kernel density estimators were exploited to estimate the probability density function of background intensity in training sequence.

该模型采用分箱核密度估计算法从训练图像序列中得到背景的密度函数。

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Under the general condition, the usual estimators of Lorenz curve and Gini coefficient are proved to possess strong consistency respectively.

并在十分广泛的条件下，论证了基于样本数据所得到的洛伦兹曲线和基尼系数的估计量都具有强相合性。

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methods we fit parameter model applying the theory of nonlinear mixed effects model, consider the characteristic of the kinds of data, carry out the parameter estimations using maximum likelihood estimators.

方法结合重复测量数据特点，采用最大似然原理进行参数估计，建立非线性混合效应参数模型。

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We also utilize the test statistic to construct the estimators for the number, locations, and jump sizes of the change points in volatility. The asymptotic properties of these estimators are derived.

我们构造了变点的个数、位置以及跳跃幅度的估计量，并且给出了这些估计量的渐近性质。

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