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本文研究了下列变系数混合效应模型: $y_{ij}=z_{ij}^{\tau}b_i+x_{ij}^{\tau}\beta(w_{ij}) +\xe_{ij},\;i=1,\cdots,m;\;j=1,\cdots,n_i$, 其中$b_i$为i.i.d.期望为$\xt$, 协方差阵为$\xs^2_bI_q$的随机效应向量, $\xe_{ij}$是i.i.d.期望为零, 具有有限方差的随机误差. 文中我们不仅给出了函数系数向量$\xb(\cdot)$的局部多项式估计, 同时给出了随机效应期望、方差和随机误差方差的估计, 并给出了这些估计量的渐进正态性和相合性, 研究结果表明了这些估计量的可靠性. 相似文献
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考虑纵向数据下一类半参数混合效应模型.应用核权函数法以及矩估计法给出了总体效应和个体效应的估计.在一般的条件下,证明了总体效应估计的渐近正态性,并给出该估计的置信区域.对总体效应和个体效应的估计进行了模拟研究,模拟显示估计效果较好. 相似文献
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YanZaizai MaJunling NieZankan 《高校应用数学学报(英文版)》2000,15(4):419-424
In this paper, a new estimator of the shape parameter in the family of Gamma distribution is constructed by using the moment idea, and it is proved that this estimator is strongly consistent and asymptotically normal. 相似文献
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对混合位置分布族,当混合比已知时,提出了关于分量参数的假设检验和区间估计方法,所提出的方法基于广义枢轴模型.在一定的条件下,检验的实际水平等于名义水平,且各置信域的实际覆盖率等于名义覆盖率.在更一般的场合,检验是相合的,并且各置信域的实际覆盖率趋于名义覆盖率.模拟显示所给的方法是令人满意的. 相似文献
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本文用[1]发展的计数过程去研究截断样本下强率函数核估计的渐进正态性.在弱于[7]和[10]的条件下,得到了更一般的结果.接着我们将这种方法运用到密度函数核估计,在较弱的条件下,得到了截断样本下密度函数核估计的渐进正态性. 相似文献
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左截断右删失数据下半参数模型风险率函数估计 总被引:3,自引:0,他引:3
文章给出了右删失左截断数据半参数模型下的风险率函数估计,讨论了风险率函数估计的渐近性质,获得了这些估计的渐近正态性,对数律和重对数律.由于假定删失机制服从半参数模型下,从而知道模型的更多信息,因此对于给出参数的极大似然估计,可以改进风险率函数估计的渐近性质.也就是说,删失数据模型具有半参数的辅助信息下, 风险率函数估计的渐近方差比通常的完全非参数的估计的渐近方差更小.这说明加入了额外的信息提高了风险率函数估计的效率. 相似文献
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截断数据是生存分析的重要研究内容,而以往关于截断数据的讨论仅限于离散场合.本文将截断数据的问题推广到连续场合,对右截断下连续过程的生存函数的估计进行了讨论,并进一步证明了估计量的强相合性. 相似文献
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在流行病学,生物统计学和天文学中常遇到随机截断数据.在随机截断下,人们关心的随机变量X被另一个随机变量y干扰.只有当X≥y时,才能观测到X和Y.在这个模型下,人们需要用截断数据估计X的分布函数F.本文证明,F的非参数最大似然估计Fn在下述意义下服从中心极限定理.对任何可测函数g(x),√n∫f9(x)[dFn(x)-dF(x)]依分布收敛到均值为零方差为σ2的正态分布.从这个结果可以得出F的各种矩,特征函数等估计的渐近正态性.作为推论,还可以得到Fn在整个直线上的依分布收敛.我们的结果不要求X和Y的分布函数连续,得到的方差公式是简明的. 相似文献
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对一维一阶一个门限的TAR模型,通过模型所构成的Markov链的遍历性,得到了其核密度估计的渐近无偏性,均方相容性和渐近正态性 相似文献
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随机自变量多项式回归函数的估计问题 总被引:2,自引:0,他引:2
以随机函数逼近论的观点看待统计模型问题,得到了解决其问题的具有一般性的新途径与新方法,作出了随机自变量多项式回归函数及其相应的误差方差的优良估计,其优良性的标准包括强相合性、相合渐近正态性与L1收敛性. 相似文献
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Qihua Wang 《Journal of multivariate analysis》2000,74(2):2463
Consider the linear models of the form Y=Xτβ+ with the response Y censored randomly on the right and X measured erroneously. Without specifying any error models, in this paper, a semiparametric method is applied to the estimation of the parametric vector β with the help of proper validation data. For the proposed estimator, an asymptotic representation is established and the asymptotic normality is also proved. 相似文献
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Toshio Honda 《Annals of the Institute of Statistical Mathematics》2000,52(4):599-611
We estimate the marginal density function of a long-range dependent linear process by the kernel estimator. We assume the innovations are i.i.d. Then it is known that the term of the sample mean is dominant in the MISE of the kernel density estimator when the dependence is beyond some level which depends on the bandwidth and that the MISE has asymptotically the same form as for i.i.d. observations when the dependence is below the level. We call the latter the case where the dependence is not very strong and focus on it in this paper. We show that the asymptotic distribution of the kernel density estimator is the same as for i.i.d. observations and the effect of long-range dependence does not appear. In addition we describe some results for weakly dependent linear processes. 相似文献
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《Journal of computational and graphical statistics》2013,22(2):461-478
We propose using minimum distance to obtain nonparametric estimates of the distributions of components in random effects models. A main setting considered is equivalent to having a large number of small datasets whose locations, and perhaps scales, vary randomly, but which otherwise have a common distribution. Interest focuses on estimating the distribution that is common to all datasets, knowledge of which is crucial in multiple testing problems where a location/scale invariant test is applied to every small dataset. A detailed algorithm for computing minimum distance estimates is proposed, and the usefulness of our methodology is illustrated by a simulation study and an analysis of microarray data. Supplemental materials for the article, including R-code and a dataset, are available online. 相似文献
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M. S. Ginovian 《Acta Appl Math》2003,78(1-3):145-154
The paper considers a problem of construction of asymptotically efficient estimators for functionals defined on a class of spectral densities. We define the concepts of H
0- and IK-efficiency of estimators, based on the variants of Hájek–Ibragimov–Khas'minskii convolution theorem and Hájek–Le Cam local asymptotic minimax theorem, respectively. We prove that
is a suitable sequence of T
1/2-consistent estimators of unknown spectral density (), is H
0- and IK-asymptotically efficient estimator for a nonlinear smooth functional (). 相似文献
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A wavelet method of detection and estimation of change points in nonparametric regression models under random design is proposed. The confidence bound of our test is derived by using the test statistics based on empirical wavelet coefficients as obtained by wavelet transformation of the data which is observed with noise. Moreover, the consistence of the test is proved while the rate of convergence is given. The method turns out to be effective after being tested on simulated examples and applied to IBM stock market data. 相似文献