共查询到19条相似文献,搜索用时 62 毫秒
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线性模型M估计分布的Bootstrap逼近的强收敛 总被引:2,自引:0,他引:2
本文讨论标准线性模型M估计分布的随机加权逼近,建立了随机加权M估计的线性表示及Bootstrap强逼近,同时还得到了逼近的一致强收敛速度,其主要部分的阶在Berry-Esseen意义下已达最优. 相似文献
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半参数回归模型中随机加权M估计的强逼近 总被引:4,自引:0,他引:4
用随机加权法给出了半参数回归模型中参数的随机加权M估计,在一般的条件下证明了用随机加权统计量的分布逼近原估计量误差的分布的强有效性,并给出了M估计的最优强收敛速度。 相似文献
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涂冬生 《数学物理学报(A辑)》1992,12(2):226-233
本文利用对样本随机加权的思想,构造了线性模型中误差方差估计的抽样分布的一种新的逼近,与传统的Boostrop方法相比,随机加权逼近不需要样本独立同分布的假设,在很广泛的条件下,我们证明了新逼近方法的相合性。 相似文献
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序集抽样是一种适用于准确测量花费太高而排序费用可以忽略不记时的一种抽样方法.讨论了序集抽样下的对于一般分布族M估计的相合性和渐近正态性并且通过随机加权的方法来估计M估计的分布. 相似文献
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Raoand Zhao(1992)提出了一种用随机加权的方法去逼近线性回归模型中M-估计的渐近分布。之前,Fang and zhao(2002)把这种方法推广到设计阵是随机的删失回归模型.本文,我们把这个结果推广到设计阵是非随机的删失回归模型,并证明该随机加权方法的一些大样本性质。 相似文献
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设x1,x2,…xn(连续未知),Fn为经验分布函数,Hn(x)为随机加权经验分布函数,。xn1≤xn2≤…≤xnn为次序统计量.记以Fn取代σ2(J,F)中的F即得σ2(J,Fn). 相似文献
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标准化样本均值分布的随机加权逼近—多维情形 总被引:3,自引:0,他引:3
本文考虑多维标准化样本均值分布的随机加权逼近,得到了O((?)~(-1/2))的最优精度,从而拓广了随机加权法的应用范围. 相似文献
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半参数回归模型中小波估计的随机加权逼近速度 总被引:10,自引:1,他引:9
把小波光滑方法和随机加权方法结合在一起,获得了半参数回归模型中参数分量的小波估计的随机加权逼近速度为σ(n^-1/2)。因此,从大样本意义上说,小波光滑方法和随机加权方法对半参数回归模型是可用的。 相似文献
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Xiao-yan WU Ya-ning YANG & Lin-cheng ZHAO Department of Statistics Finance University of Science Technology of China Hefei China 《中国科学A辑(英文版)》2007,50(1):87-99
The M-test has been in common use and widely studied in testing the linear hypotheses in linear models. However, the critical value for the test is usually related to the quantities of the unknown error distribution and the estimate of the nuisance parameters may be rather involved, not only for the M-test method but also for the existing bootstrap methods. In this paper we suggest a random weighting resampling method for approximating the null distribution of the M-test statistic. It is shown that, under both the null and the local alternatives, the random weighting statistic has the same asymptotic distribution as the null distribution of the M-test. The critical values of the M-test can therefore be obtained by the random weighting method without estimating the nuisance parameters. A distinguished feature of the proposed method is that the approximation is valid even the null hypothesis is not true and the power evaluation is possible under the local alternatives. 相似文献
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Christensen O. F. Møller J. Waagepetersen R. P. 《Methodology and Computing in Applied Probability》2001,3(3):309-327
Conditional simulation is useful in connection with inference and prediction for a generalized linear mixed model. We consider random walk Metropolis and Langevin-Hastings algorithms for simulating the random effects given the observed data, when the joint distribution of the unobserved random effects is multivariate Gaussian. In particular we study the desirable property of geometric ergodicity, which ensures the validity of central limit theorems for Monte Carlo estimates. 相似文献
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应用Monte Carlo EM(MCEM)算法给出了多层线性模型参数估计的新方法,解决了EM算法用于模型时积分计算困难的问题,并通过数值模拟将方法的估计结果与EM算法的进行比较,验证了方法的有效性和可行性. 相似文献
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基于OLS估计残差,本文将Bootstrap方法用于空间误差相关性LM-Error检验,综合考虑Bootstrap模拟抽样次数、空间衔接结构以及样本量,研究并比较空间误差相关Bootstrap LM-Error检验与渐近检验的水平扭曲。大量Monte Carlo实验结果显示,当模型误差不满足独立正态分布的假设条件时,空间误差相关LM-Error渐近检验的水平扭曲较大,采用Bootstrap方法可以较好地降低该水平扭曲;不管模型误差是否满足独立正态分布的假设条件,Bootstrap方法均能够有效地降低LMError渐近检验的水平扭曲。 相似文献
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The exponential random graph model (ERGM) plays a major role in social network analysis. However, parameter estimation for the ERGM is a hard problem due to the intractability of its normalizing constant and the model degeneracy. The existing algorithms, such as Monte Carlo maximum likelihood estimation (MCMLE) and stochastic approximation, often fail for this problem in the presence of model degeneracy. In this article, we introduce the varying truncation stochastic approximation Markov chain Monte Carlo (SAMCMC) algorithm to tackle this problem. The varying truncation mechanism enables the algorithm to choose an appropriate starting point and an appropriate gain factor sequence, and thus to produce a reasonable parameter estimate for the ERGM even in the presence of model degeneracy. The numerical results indicate that the varying truncation SAMCMC algorithm can significantly outperform the MCMLE and stochastic approximation algorithms: for degenerate ERGMs, MCMLE and stochastic approximation often fail to produce any reasonable parameter estimates, while SAMCMC can do; for nondegenerate ERGMs, SAMCMC can work as well as or better than MCMLE and stochastic approximation. The data and source codes used for this article are available online as supplementary materials. 相似文献