共查询到17条相似文献，搜索用时 187 毫秒
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本文在参数的先验分布为逆伽玛先验分布条件下研究广义Pareto分布参数的Bayes估计问题,并在平方误差损失函数下,导出了参数的Bayes估计。文末通过Monte Carlo数值模拟试验对极大似然估计和Bayes估计进行了比较。 相似文献
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对于一个参数进行估计来讲,不仅要考虑估计的精度,还要考虑所得到的估计对于模型拟合的优良程度。通常的损失函数无能为力,而平衡损失函数可以考虑到这两点,本文将在平衡误差损失函数下研究广义Pareto分布参数的Bayes估计问题。在平衡损失函数下导出了参数的Bayes估计并讨论了一类线性形式估计的可容许性和不可容许性。 相似文献
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在指数分布定数截尾情形下,当先验分布中的超参数部分未知时,在加权平方损失下构造了刻度参数的参数型经验Bayes(PEB)估计,研究了其在均方误差(MSE)准则下相对于一致最小方差无偏估计(UMVUE)的优良性,并获得了PEB估计的大样本性质.当先验分布中的超参数完全未知时,通过数值模拟比较了PEB估计和UMVUE的均方误差,获得了其优良性.最后,通过数值模拟的结果,获得了PEB区间估计的优良性. 相似文献
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正本文研究了产品寿命服从指数分布情形的寿命绩效指标的Bayes统计推断问题。基于参数的先验分布为伽玛先验分布,得到了加权平方误差损失函数下寿命绩效指标的Bayes估计,此外根据该估计提出了一套产品质量检验程序用于产品质量检测。最后实例分析结论说明了检验方法的有效性。 相似文献
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本文在加权平方损失下导出了平衡的双向分类随机效应模型中方差分量的Bayes估计,并利用 非参数方法构造了方差分量的经验Bayes (EB)估计。在适当的条件下证明 了EB估计的收敛速度。最后,给出一个满足主要结果的例子。 相似文献
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多源验前信息下k/N(G)系统可靠性指标的Bayes估计 总被引：1，自引：0，他引：1
针对k/n(G)系统部件寿命中的参数存在多源验前信息的情况下,基于ML-Ⅱ信息融合方法对先验分布进行融合,并在平方损失函数和LINEX损失函数下,分别得到了k/n(G)系统可靠性指标的Bayes估计。最后的随机模拟例子表明,该方法评估效果好,精度高,并且LINEX损失函数下的估计要优于平方损失函数下的估计。 相似文献
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泊松回归模型常常用于计数数据的研究中,然而在实际数据中零值的比例可能远远大于泊松分布中取零值的概率,而且这些零值通常都有其特殊含义.此外计数数据可能是分组数据,即观测到的数据不是确切值而只是已知其落在某一个区间范围之内;或者某些特定的数据,例如工资,要先对它进行人为的分组然后再进行分析.考虑一种零膨胀泊松半参数回归模型来处理上述分组计数数据.该模型中泊松分布的期望与协变量之间采用部分线性连接函数,而零值的概率与协变量之间采用线性连接函数.利用Sieve极大似然估计方法来估计该回归模型中参数和非参数函数,并提出了一种得分检验方法来检验是否存在零膨胀.在一定正则条件下,获得了Sieve极大似然估计的渐近性质,证明了参数部分的估计是强相合,渐近正态及渐近有效的;同时非参数函数的估计达到了最优收敛速度.模拟研究表明,估计和检验方法效果都比较好,最后将此模型和推断方法应用于一组公共卫生领域实际数据研究. 相似文献
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运用贝叶斯决策方法,在定时和定数截尾寿命试验下,引入两种损失函数,总结和进一步推断指数寿命分布的分布参数取各种不同先验分布形式时的贝叶斯估计。 相似文献
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在“加权线性损失”下讨论刻度指数族中参数的经验Ｂａｙｅｓ（ＥＢ）检验问题．利用基于 Ｂｅｓｓｅｌ函数的核估计方法构造了ＥＢ检验函数．在适当的条件下证明了获得的ＥＢ检验函数是渐近最优的具有收敛速度Ｏ（ｎ－１ｌｎ６ ｎ）．最后给出一个满足定理条件的例子． 相似文献
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从均匀分布的密度函数和分布函数出发,根据两个总体的独立性,利用微积分公式给出两个均匀分布区间长度比的估计量及其相关的性质。通过已构造的统计量得出了完善的两均匀分布区间长度比的区间估计。 相似文献
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《Information processing & management》2020,57(5):102221
Although statistical learning methods have achieved success in e-commerce platform product review sentiment classification, two problems have limited its practical application: 1) The computational efficiency to process large-scale reviews; 2) the ability to continuously learn from increasing reviews and multiple domains. This paper presents a continuous naïve Bayes learning framework for large-scale and multi-domain e-commerce platform product review sentiment classification. While keeping the high computational efficiency of the traditional naïve Bayes model, we extend the parameter estimation mechanism in naïve Bayes to a continuous learning style. We furthermore propose ways to fine-tune the learned distribution based on three kinds of assumptions to better adapt to different domains. Experimental results on the Amazon product and movie review sentiment datasets show that our model can use the knowledge learned from past domains to guide learning in new domains, and has a better capacity of dealing with reviews that are continuously updated and come from different domains. 相似文献
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Peng Yun Panlong Wu Xingxiu Li Shan He Kai Wu 《Journal of The Franklin Institute》2021,358(8):4503-4527
For target tracking systems, the probability of detecting a target is difficult to determine, and the process noise often has non-Gaussian heavy-tailed characteristics owing to interference from outliers. To address the issues associated with single target tracking within clutters in scenarios with an unknown detection probability and heavy-tailed process noise, this paper presents a variational Bayesian-based adaptive probabilistic data association filter (VB-APDAF). The beta distribution, Pearson type VII distribution and multinomial distribution are used to model the detection probability, the process noise, and the association events, respectively. To guarantee the conjugation, a novel parameter estimation strategy is employed. In this strategy, the previous state is introduced in the state update process to construct the joint probability density function of parameters to be estimated and data set. The VB framework is used to estimate the target state, detection probability, and associated events. An experiment was performed under simulated conditions to demonstrate the effectiveness of the proposed filter. 相似文献
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在针对基础学科、工程技术、社会科学领域的多学科期刊引文密度进行分析的基础上,引入学科期刊平均引文密度与学科期刊最可几引文密度表征学科期刊的引文密度水平。使用对数正态分布函数拟合学科期刊引文密度的频数分布显示,拟合峰值接近学科期刊最可几引文密度。最后,提出对数正态分布函数的特征参数可以表征学科期刊引文密度水平的观点。 相似文献
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Xianqiang Yang Weili Xiong Junxia Ma Zeyuan Wang 《Journal of The Franklin Institute》2017,354(13):5678-5693
This paper considers the parameter estimation for Wiener time-delay systems with the output data contaminated with outliers. The time-delay and corrupted output data bring great challenges to the parameter estimation problem. The statistical model of the estimation problem is constructed based on the Laplace distribution and the identification problem is formulated in the scheme of the expectation-maximization (EM) algorithm. The negative effect of outliers imposed on the parameter estimation problem is sufficiently suppressed and the unknown time-delay and model parameters can be estimated simultaneously. The simulation example is given to demonstrate the effectiveness of the proposed algorithm. 相似文献
16.
Parameter fault detection and estimation of a class of nonlinear systems using observers 总被引：1，自引：0，他引：1
The focus of this paper is on the detection and estimation of parameter faults in nonlinear systems with nonlinear fault distribution functions. The novelty of this contribution is that it handles the nonlinear fault distribution function; since such a fault distribution function depends not only on the inputs and outputs of the system but also on unmeasured states, it causes additional complexity in fault estimation. The proposed detection and estimation tool is based on the adaptive observer technique. Under the Lipschitz condition, a fault detection observer and adaptive diagnosis observer are proposed. Then, relaxation of the Lipschitz requirement is proposed and the necessary modification to the diagnostic tool is presented. Finally, the example of a one-wheel model with lumped friction is presented to illustrate the applicability of the proposed diagnosis method. 相似文献
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The purpose of fault diagnosis of stochastic distribution control (SDC) systems is to use the measured input and the system output probability density functions (PDFs) to obtain the fault information of the SDC system. When the target PDF is known, the purpose of fault tolerant control of stochastic distribution control system is to make the output PDF still track the given distribution using the fault tolerant controller. However, in practice, time delay may exist in the data (or image) processing, the modeling and transmission phases. When time delay is not considered, the effectiveness of the fault detection, diagnosis and fault tolerant control of stochastic distribution systems will be reduced. In this paper, the rational square-root B-spline is used to approach the output probability density function. In order to diagnose the fault in the dynamic part of such systems, it is then followed by the novel design of a nonlinear neural network observer-based fault diagnosis algorithm. The time delay term will be deleted in the stability proof of the observation error dynamic system. Based on the fault diagnosis information, a new fault tolerant controller based on PI tracking control is designed to make the post-fault probability density function still track the given distribution, which is dependent of the time delay term. Finally, simulations for the particle distribution control problem are given to show the effectiveness of the proposed approach. 相似文献