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在响应变量满足MAR缺失机制下,我们分别研究了基于观察到的完全样本数据对、基于固定补足后的“完全洋本”和基于分数线性回归填补后的“完全洋本”得到的回归系数的最小二乘估计的弱相合性、强相合性及渐近正态性,我们还通过数值模拟,比较了基于上述估计得到的β的置信区间的优劣。 相似文献
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目的对医院出院病人调查表普遍存在的数据缺失进行填补与分析,以保证统计调查表的质量,为医院以及上级卫生部门了解现状,进行预策和决策提供技术支持和质量保证。方法运用SAS9.1,采用多重填补方法Markov Chain Monte Carlo(MCMC)模型对缺失数据进行多次填补并综合分析。结果MCMC填补10次的结果最优。结论(Multiple Imputation)MI方法在解决医院出院病人调查表数据缺失时有优势,发挥空间较大,且填补效率较高。 相似文献
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在缺失数据机制是可忽略的假设下,导出了有单调缺失数据的条件独立正态模型中协方差阵和精度阵的Cholesky分解的最大似然估计和无偏估计.通过引入一类特殊的变换群并在更广义的损失下,获得了其最优同变估计.这表明最大似然估计和无偏估计是非容许的.最后,通过数值模拟验证了相关结果的有效性. 相似文献
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数据缺失在实际应用中普遍存在,数据缺失会降低研究效率,导致参数估计有偏.在协变量随机缺失(MAR)的假定下,本文基于众数回归和逆概率加权估计方法对线性模型进行参数估计.该方法结合参数Logistic回归和非参数Nadaraya-Watson估计两种倾向得分估计方法,分别构建IPWM-L估计量和IPWM-NW估计量.模拟研究和实例分析表明,众数回归模型比均值回归模型更具稳健性,逆概率加权众数(IPWM)估计方法在缺失数据下表现出了更好的拟合效果,与IPWM-L估计量相比, IPWM-NW估计量更稳健. 相似文献
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本文对两个样本数据不完全的线性模型展开讨论,
其中线性模型协变量的观测值不缺失, 响应变量的观测值随机缺失(MAR).
我们采用逆概率加权填补方法对响应变量的缺失值进行补足, 得到两个线性回归模型``完全'样本数据,
在``完全'样本数据的基础上构造了响应变量分位数差异的对数经验似然比统计量.
与以往研究结果不同的是本文在一定条件下证明了该统计量的极限分布为标准,
降低了由于权系数估计带来的误差, 进一步构造出了精度更高的分位数差异的经验似然置信区间. 相似文献
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《数学的实践与认识》2020,(4)
在随机缺失(MAR)机制下利用经验似然方法构造了线性回归模型中误差方差的估计.并在一定条件下,证明了该估计的渐近正态性,由此得出当误差的分布不对称时,该估计的渐近方差比常用估计的渐近方差小. 相似文献
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Tatsuya Kubokawa 《Journal of multivariate analysis》1998,67(2):169-189
The problem of estimating the common regression coefficients is addressed in this paper for two regression equations with possibly different error variances. The feasible generalized least squares (FGLS) estimators have been believed to be admissible within the class of unbiased estimators. It is, nevertheless, established that the FGLS estimators are inadmissible in light of minimizing the covariance matrices if the dimension of the common regression coefficients is greater than or equal to three. Double shrinkage unbiased estimators are proposed as possible candidates of improved procedures. 相似文献
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在回归模型中,对一类因变量函数的条件期望方程的附加信息,我们提出了基于极大经验似然方法的局部线性点估计,在一定条件下证明了这些估计的相合性和渐近正态性,而且估计的方差小于通常不带附加信息核估计的方差.模拟结果也显示了估计的优良性. 相似文献
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This note discusses the asymptotic distribution of two scale and location invariant estimators of two scale parameters in the multiple linear regression model. Both of these estimators need an initial estimator of the regression parameter vector. The asymptotic distribution of one of these estimators does not depend on this initial estimator. Both of these estimators are useful in the computation of scale and translation invariant adaptive estimators and M-estimators of the regression parameter vector. 相似文献
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This article investigates linear minimax estimators of regression coefficient in a linear model with an assumption that the underlying distribution is a normal one with a nonnegative definite covariance matrix under a balanced loss function. Some linear minimax estimators of regression coefficient in the class of all estimators are obtained. The result shows that the linear minimax estimators are unique under some conditions. 相似文献
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Philip E Cheng 《Journal of multivariate analysis》1984,15(1):63-72
For a well-known class of nonparametric regression function estimators of nearest neighbor type the uniform measure of deviation from the estimators to the true regression function is studied. Under weak regularity conditions it is shown that the estimators are uniformly consistent with probability one and the corresponding rate of convergence is near-optimal. 相似文献
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We consider the problem of estimating the unknown parameters of linear regression in the case when the variances of observations depend on the unknown parameters of the model. A two-step method is suggested for constructing asymptotically linear estimators. Some general sufficient conditions for the asymptotic normality of the estimators are found, and an explicit form is established of the best asymptotically linear estimators. The behavior of the estimators is studied in detail in the case when the parameter of the regression model is one-dimensional. 相似文献
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首先给出非零截距线性模型T-型估计的模型与EM算法,其次给出非线性回归模型参数的T-型估计,利用泰勒级数对模型线性化,得到参数估计的迭代算法,最后用数值模拟实验验证了该算法的正确性和证实了T-型估计的稳健性. 相似文献
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讨论了在强相关数据情形下对回归函数的小波估计,并且给出了估计量的均方误差的一个渐近展开表示式. 对研究估计量的优劣,所推导的近似表示式显得非常重要.对一般的回归函数核估计,如果回归函数不是充分光滑,这个均方误差表示式并不成立A·D2但对小波估计,即使回归函数间断连续,这个均方误差表示式仍然成立.因此,小波估计的收敛速度要比核估计来得快,从而小波估计在某种程度上改进了现有的核估计. 相似文献
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We consider the nonparametric regression model with long memory data that are not necessarily Gaussian and provide an asymptotic
expansion for the mean integrated squared error (MISE) of nonlinear wavelet-based mean regression function estimators. We
show this MISE expansion, when the underlying mean regression function is only piecewise smooth, is the same as analogous
expansion for the kernel estimators. However, for the kernel estimators, this MISE expansion generally fails if an additional
smoothness assumption is absent.
Research supported in part by the NSF grant DMS-0103939. 相似文献