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1.
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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非线性回归模型M估计的迭代公式及其收敛性 总被引:1,自引:0,他引:1
本文研究了非线性回归模型M估计的Gauss-Newton迭代公式及其改进形式的收敛性问题。把Jeunrich和Gallant等人关于最小二乘估计的结果推广到M估计的情形。本文的证明显示,这些结果还可以推广到更广泛的模型和更一般的估计。本文的实例说明,改进的Gauss-Newton迭代法对于求解非线性回归的M估计是比较有效的,M估计对于消除异常点的影响育显著的作用。 相似文献
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研究了混合样本线性模型中的M估计,在较弱的矩条件下,获得了M估计是强相合估计的充分条件.与相应结论比较,有了较大的实质性改进. 相似文献
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(ρ)混合样本线性模型M估计的强相合性 总被引:1,自引:0,他引:1
研究了(ρ)混合样本线性模型中的M估计,在较弱的矩条件下,获得了M估计是强相合估计的充分条件.与相应结论比较,有了较大的实质性改进. 相似文献
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本文在非凸的情况下,研究了线性回归参数的M估计的强、弱线性表示。所得余项估计的阶或阶的主部是确切的。作为应用,导出了M估计的收敛速度、重对数律及Berry-Esseen型界限。 相似文献
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本文研究线性模型中回归参数M估计的强相合性,给出一些较弱的充分条件.与相应结论比较,这里给出的条件对矩的要求有实质性的改进. 相似文献
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Margarida Brito Ana Cristina Moreira Freitas 《Insurance: Mathematics and Economics》2008,43(2):203-208
We establish an Edgeworth expansion for an estimator of the adjustment coefficient R, directly related to the geometric-type estimator for general exponential tail coefficients, proposed in [Brito, M., Freitas, A.C.M., 2003. Limiting behaviour of a geometric-type estimator for tail indices. Insurance Math. Econom. 33, 211-226].Using the first term of the expansion, we construct improved confidence bounds for R. The accuracy of the approximation is illustrated using an example from insurance (cf. [Schultze, J., Steinebach, J., 1996. On least squares estimates of an exponential tail coefficient. Statist. Dec. 14, 353-372]). 相似文献
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The moment estimator has been widely used in extreme value theory in order to estimate the extreme value index, however it is not location invariant. In this paper, based on the moment-type estimator, we propose a new location invariant moment-type estimator,and discuss its asymptotic normality under the second order regular variation. Finally, a simulation is presented to compare this new estimator with another location invariant momenttype estimator γ_n~M(k_0, k) proposed by Ling, which indicates that the new estimator has good performances. 相似文献
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In this paper, we propose an exponential ratio type estimator of the finite population mean when auxiliary information is qualitative in nature. Under simple random sampling without replacement scheme, the expressions for the bias and the mean square error of the proposed estimator have been obtained, up to first order of approximation. To show that our proposed estimator is more efficient as compared to the existing estimators, we have made a comparative study with respect to their mean square errors. Theoretically and numerically, we have found that our proposed estimator is always more efficient as compared to its competitor estimators including all the estimators of Abd-Elfattah et al. [1] [A.M. Abd-Elfattah, E.A. El-Sherpieny, S.M. Mohamed, and O.F. Abdou. Improvement in estimating the population mean in simple random sampling using information on auxiliary attribute. Applied Mathematics and Computation, 215 (2010), 4198-4202]. 相似文献
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L. Boulaajine M. Farhloul L. Paquet 《Numerical Methods for Partial Differential Equations》2005,21(5):938-960
In this article, we propose a residual based reliable and efficient error estimator for the new dual mixed finite element method of the elasticity problem in a polygonal domain, introduced by M. Farhloul and M. Fortin. With the help of a specific generalized Helmholtz decomposition of the error on the strain tensor and the classical decomposition of the error on the gradient of the displacements, we show that our global error estimator is reliable. Efficiency of our estimator follows by using classical inverse estimates. The lower and upper error bounds obtained are uniform with respect to the Lamé coefficient λ, in particular avoiding locking phenomena. © 2005 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2005. 相似文献
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在一些较弱的充分条件下,本文研究了误差为随机适应序列下,线性模型回归参数M估计的强相合性.与文献中已有结果比较,扩大了应用范围,且对矩条件也有较大改进.同时我们给出了随机适应误差下线性模型参数M估计的渐近正态性. 相似文献
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Yoshikazu Takada 《Annals of the Institute of Statistical Mathematics》1979,31(1):177-183
Summary Stein's positive part estimator forp normal means is known to dominate the M.L.E. ifp≧3. In this article by introducing some proirs we show that Stein's positive part estimator is posterior mode. We also consider
the Bayes estimators (posterior mean) with respect to the same priors and show that some of them dominate M.L.E. and are admissible. 相似文献
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Akio Suzukawa Hideyuki Imai Yoshiharu Sato 《Annals of the Institute of Statistical Mathematics》2001,53(2):262-276
This paper is intended as an investigation of parametric estimation for the randomly right censored data. In parametric estimation, the Kullback-Leibler information is used as a measure of the divergence of a true distribution generating a data relative to a distribution in an assumed parametric model M. When the data is uncensored, maximum likelihood estimator (MLE) is a consistent estimator of minimizing the Kullback-Leibler information, even if the assumed model M does not contain the true distribution. We call this property minimum Kullback-Leibler information consistency (MKLI-consistency). However, the MLE obtained by maximizing the likelihood function based on the censored data is not MKLI-consistent. As an alternative to the MLE, Oakes (1986, Biometrics, 42, 177–182) proposed an estimator termed approximate maximum likelihood estimator (AMLE) due to its computational advantage and potential for robustness. We show MKLI-consistency and asymptotic normality of the AMLE under the misspecification of the parametric model. In a simulation study, we investigate mean square errors of these two estimators and an estimator which is obtained by treating a jackknife corrected Kaplan-Meier integral as the log-likelihood. On the basis of the simulation results and the asymptotic results, we discuss comparison among these estimators. We also derive information criteria for the MLE and the AMLE under censorship, and which can be used not only for selecting models but also for selecting estimation procedures. 相似文献
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We consider the estimation of parameters in stochastic differential equations (SDEs). The problem is treated in the setting of nonlinear filtering theory with a degenerate diffusion matrix. A robust stochastic Feynman–Kac representation for solutions of SDEs of Zakai-type is derived. It is verified that these solutions are conditional densities for the conditional measures defined by degenerate filtering problems. We show that the corresponding estimator for the parameters is robust in the following sense: It depends continuously on both the measurement path and on the intensity of the measurement noise. An algorithm based on a Monte-Carlo approach is given for the practical application of the estimator, and numerical results are reported.
Mathematics Subject Classifications (2000) Primary: 62M05, 62M20; secondary: 62F15. 相似文献
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《Comptes Rendus Mathematique》2008,346(17-18):999-1002
We study an adaptive estimator of the spectral density by projection. We show that this estimator reaches a superoptimal rate on a dense set in the spectral densities class, and a quasi-optimal rate elsewhere. This set can be chosen by the Statistician, and the superoptimal speed is reached for integrated quadratic error and almost sure uniform convergence. As an application we obtain a consistent estimator of a moving average order. To cite this article: M. Souare, C. R. Acad. Sci. Paris, Ser. I 346 (2008). 相似文献