共查询到18条相似文献,搜索用时 93 毫秒
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提出了奇异线性模型中参数β的最佳线性无偏估计(BLUE)相对于最小二乘估计(LSE)的一种新的相对效率,并给出了该相对效率的下界,最后讨论了该相对效率与广义相关系数的关系. 相似文献
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线性模型参数估计的一种新的相对效率 总被引:2,自引:0,他引:2
本文对线性模型的最小二乘估计(LSE)与最佳线性无偏估计(BLUE)提出了一种新的相对效率并给出了新的相对效率的上、下界,最后还讨论了新的相对效率与已有的几种相对效率的关系。 相似文献
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对于聚集数据的多元线性模型,提出了参数的多元聚集广义岭估计的概念,给出了多元聚集广义岭估计相对于最小二乘估计及最佳线性无偏估计的两种相对效率,并得到了这两种相对效率的上界. 相似文献
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周永正 《数学的实践与认识》2013,43(1)
对于聚集数据的线性模型,本文给出了参数β的聚集改进Liu估计,研究了该估计相对于最小二乘估计和相对于Peter-Karsten估计的两种相对效率,并得到了相对效率的上界. 相似文献
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权回归模型中最小二乘估计的相对效率 总被引:4,自引:0,他引:4
对于线性加权回归模型,本文得到了未知参数的最小二乘估计相对于最佳线性无偏估计的四种相对效率的下界,并建立了相对效率与广义相关系数的联系。 相似文献
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对于聚集数据的多元线性模型,提出了参数的多元聚集综合岭估计的概念,给出了多元聚集综合岭估计相对于最小二乘估计及最佳线性无偏估计的两种相对效率,并得到了这两种相对效率的上界.应用Monte Carlo模拟,验证了有关结论是合理的. 相似文献
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Harter H_L.,Balakrishnan N.等先后讨论了Logistic总体分布参数的极大似然估计,近似极大似然估计;其后Ogawa J.,Lloyd E.H.,Kulldorff G.,Gupta S.S,及chan L.K. 等又先后讨论了Logistlic分布参数的最佳线性无偏估计及估计的相对效率等问题.令人遗憾的是:在大样本情形下,上述估计均难以求得.为缓解这一困难,本文讨论利用样本分位数的Logistic总体的近似最佳线性无偏估计,给出估计量的大样本性质,以及样本分位数不超过10情形下,估计量有渐近最大相对估计效率时样本分位数的选取方案等. 相似文献
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错误先验假定下Bayes线性无偏估计的稳健性 总被引:1,自引:0,他引:1
本文基于错误的先验假定获得了一般线性模型下可估函数的Bayes线性无偏估计(BLUE), 证明了在均方误差矩阵(MSEM)准则和后验Pitman Closeness (PPC)准则下BLUE相对于最小二乘估计(LSE)的优良性, 并导出了它们的相对效率的界, 从而获得BLUE的稳健性. 相似文献
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This paper studies improvements of multivariate local linear regression. Two intuitively appealing variance reduction techniques are proposed. They both yield estimators that retain the same asymptotic conditional bias as the multivariate local linear estimator and have smaller asymptotic conditional variances. The estimators are further examined in aspects of bandwidth selection, asymptotic relative efficiency and implementation. Their asymptotic relative efficiencies with respect to the multivariate local linear estimator are very attractive and increase exponentially as the number of covariates increases. Data-driven bandwidth selection procedures for the new estimators are straightforward given those for local linear regression. Since the proposed estimators each has a simple form, implementation is easy and requires much less or about the same amount of effort. In addition, boundary corrections are automatic as in the usual multivariate local linear regression. 相似文献
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The asymptotic distribution for the local linear estimator in nonparametric regression models is established under a general parametric error covariance with dependent and heterogeneously distributed regressors. A two-step estimation procedure that incorporates the parametric information in the error covariance matrix is proposed. Sufficient conditions for its asymptotic normality are given and its efficiency relative to the local linear estimator is established. We give examples of how our results are useful in some recently studied regression models. A Monte Carlo study confirms the asymptotic theory predictions and compares our estimator with some recently proposed alternative estimation procedures. 相似文献
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A generalization of classical linear models is varying coefficient
models, which offer a flexible approach to modeling nonlinearity between covariates. A
method of local weighted composite quantile regression is suggested to estimate the
coefficient functions. The local Bahadur representation of the local estimator is derived
and the asymptotic normality of the resulting estimator is established. Comparing to the
local least squares estimator, the asymptotic relative efficiency is examined for the local
weighted composite quantile estimator. Both theoretical analysis and numerical simulations
reveal that the local weighted composite quantile estimator can obtain more efficient than
the local least squares estimator for various non-normal errors. In the normal error case,
the local weighted composite quantile estimator is almost as efficient as the local least
squares estimator. Monte Carlo results are consistent with our theoretical findings. An
empirical application demonstrates the potential of the proposed method. 相似文献
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Hiroshi Kurata 《Journal of multivariate analysis》1998,67(2):297-305
This paper presents a generalization of Rao's covariance structure. In a general linear regression model, we classify the error covariance structure into several categories and investigate the efficiency of the ordinary least squares estimator (OLSE) relative to the Gauss–Markov estimator (GME). The classification criterion considered here is the rank of the covariance matrix of the difference between the OLSE and the GME. Hence our classification includes Rao's covariance structure. The results are applied to models with special structures: a general multivariate analysis of variance model, a seemingly unrelated regression model, and a serial correlation model. 相似文献