共查询到16条相似文献,搜索用时 109 毫秒
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在无重复因析试验下,基于李济洪,王钰等(2010)提出的散度效应的AMH估计,给出了一种散度效应的迭代估计方法(称为IAMH估计),通过模拟试验验证了此方法比常用的AMH,MH估计具有更小的均方误差. 相似文献
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本文提出方差分量ANOVA估计的一种改进方法, 证明了对于一般的方差分量模型, 只要方差分量的ANOVA估计存在就可以通过此方法给出其改进形式, 并且在均方误差意义下优于ANOVA估计. 特别地, 对于单向分类随机效应模型, Kelly和Mathew[1]对ANOVA估计的改进就是我们提出的改进方法的特殊形式, 这也给出了此类改进估计在均方误差意义下优于ANOVA估计的另一种合理的解释. 同时, 本文又将此思想应用到对谱分解估计的改进上. 本文应用协方差的简单性质证明了对带有一个随机效应的方差分量模型, 当随机效应的协方差阵只有一个非零特征值时, 随机效应方差分量谱分解估计在均方误差意义下总是优于ANOVA估计. 本文最后将第三节的结论推广到广义谱分解估计下, 同时给出广义谱分解估计待定系数的一个合理的取值. 相似文献
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自从Box和Meyer首次提出无重复因析试验中散度效应的识别和估计问题, 各种散度效应的估计方法(包括迭代和非迭代)被提出. 特别地, Brenneman 和Nair 给出了这些方法的一个综述, 并且他们验证了改进的Harvey方法优于其它的方法.本文中对于对数线性模型, 一个基于多个位置模型残差平均的非迭代的散度效应估计方法在模型选择阶段被提出. 在大多数的模拟实验模型中, 本文方法具有比MH方法更小的均方误差, 且它可以应用于MH方法不适用的0或小的绝对残差情形. 我们也考虑了这个估计的理论性质, 并进行了实例分析. 相似文献
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《数学的实践与认识》2017,(24)
区组设计的平衡性是对区组设计研究的重要概念,而组间平衡是其中基于重复次数概念的一种平衡性.探讨了组间平衡性的相关哲学概念和数学性质,并且从理论上证明了组间平衡区组设计的数学判定条件,并给出了计算机验证组间平衡性的方法.作为应用,在一般象数学的试验设计模型的基础上,证明了具有组间平衡性质的广义正交表,不但使得试验因子效应的估计无偏和方差最小,而且可以使得对试验中心值或者总体均值的估计无偏和方差最小,并且在区组试验数据较大时,其估计和区组大小的分解式基本无关,保证试验的数据分析结论具有再现性. 相似文献
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本文应用经验似然方法得到了线性模型误差方差的一类新的估计,证明了估计的渐近分布为正态分布且渐近方差不超过常用的误差方差估计的渐近方差,同时给出了渐近方差的显式表达. 相似文献
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Based on the proposed states of the Metropolis-Hastings (MH) algorithm we construct a MH Importance Sampling estimator for the approximation of expectations. The new approximation scheme is asymptotically correct and numerical experiments indicate that it can outperform the classical MH Markov chain Monte Carlo estimator. (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim) 相似文献
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How to solve the inference problem of candidate database web surveys is an urgent problem to be solved in the development of web survey. In order to solve this problem, the inference method of non-probability sampling based on superpopulation pseudo design and the combined sample is proposed. A superpopulation model is firstly built up to construct pseudo weights for a survey sample of the web candidate database. The estimator of the population mean is then computed according to the combined sample composed of the survey sample of the web candidate database and a probability sample. The variance estimator of the population mean estimator is lastly derived according to the variance estimation theory of the superpopulation model. The Bootstrap and Jackknife methods are also used to compute the variance estimator. And all these variance estimation methods are compared. The research results show that the population mean estimator based on superpopulation pseudo design and the combined sample is better, and has higher efficiency than the estimator only using the probability sample and the weighted estimator only using the survey sample of the web candidate database. The variance estimator computed by using the VM1, VM2 and VM3 method are relatively better. 相似文献
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??How to solve the inference problem of candidate database web surveys is an urgent problem to be solved in the development of web survey. In order to solve this problem, the inference method of non-probability sampling based on superpopulation pseudo design and the combined sample is proposed. A superpopulation model is firstly built up to construct pseudo weights for a survey sample of the web candidate database. The estimator of the population mean is then computed according to the combined sample composed of the survey sample of the web candidate database and a probability sample. The variance estimator of the population mean estimator is lastly derived according to the variance estimation theory of the superpopulation model. The Bootstrap and Jackknife methods are also used to compute the variance estimator. And all these variance estimation methods are compared. The research results show that the population mean estimator based on superpopulation pseudo design and the combined sample is better, and has higher efficiency than the estimator only using the probability sample and the weighted estimator only using the survey sample of the web candidate database. The variance estimator computed by using the VM1, VM2 and VM3 method are relatively better. 相似文献
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Roelof Helmers I. Wayan Mangku 《Annals of the Institute of Statistical Mathematics》2009,61(3):599-628
We construct and investigate a consistent kernel-type nonparametric estimator of the intensity function of a cyclic Poisson
process in the presence of linear trend. It is assumed that only a single realization of the Poisson process is observed in
a bounded window. We prove that the proposed estimator is consistent when the size of the window indefinitely expands. The
asymptotic bias, variance, and the mean-squared error of the proposed estimator are also computed. A simulation study shows
that the first order asymptotic approximations to the bias and variance of the estimator are not accurate enough. Second order
terms for bias and variance were derived in order to be able to predict the numerical results in the simulation. Bias reduction
of our estimator is also proposed. 相似文献
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The additive–multiplicative hazards (AMH) regression model specifies an additive and multiplicative form on the hazard function for the counting process associated with a multidimensional covariate process, which contains the Cox proportional hazards model and the additive hazards model as its special cases. In this paper, we study the AMH model with current status data, where the cumulative hazard hazard function is assumed to be nonparametric and is estimated using B-splines with monotonicity constraint on the functional, while a simultaneous sieve maximum likelihood estimation is proposed to estimate regression parameters. The proposed estimator for the parameter vector is shown to be asymptotically normal and semiparametric efficient. The B-splines estimator of the functional of the cumulative hazard function is shown to achieve the optimal nonparametric rate of convergence. A simulation study is conducted to examine the finite sample performance of the proposed estimators and algorithm, and a real data example is presented for illustration. 相似文献