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1.
协方差改进法与半相依回归的参数估计 总被引:6,自引:1,他引:5
对于由两个误差项相关的线性回归方程组成的系统,本文应用协方差改进法获得了参数的一个迭代估计序列。我们证明了当协方差阵已知时,该估计序列处处收敛到最佳线性无偏估计,且它们的协方差阵在矩阵偏序意义下单调下降收敛到最佳线性无偏估计的协方差阵,该估计序列具有Pitman准则下的优良性。当协方差阵未知时,我们证明了用协方差阵的无限制估计所产生的两步估计具有无偏性、相合性和渐近正态性。在一定意义下,本文的估计优于文献中已有的一些估计。本文的结果也显示了协方差改进方法的有效性。 相似文献
2.
线性回归系统两步协方差改进估计的优良性 总被引:1,自引:0,他引:1
对于m个相依回归方程组成的线性回归系统。本文研究了两步协方差改进估计在均方误差阵意义下的优良性,即在随机误差服从正态分布的假设下,当样本量充分大时,两步协方差改进估计与协方差改进估计一样好。 相似文献
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在二次损失函数下,本文给出了正态方差最优同变估计的一个新的改进估计,并证明了常用正态协方差和协方差阵的估计都是非容许估计。 相似文献
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对由两个不相关的回归方程组成的系统(y1为m维向量,y2为n维向量,m≠n),运用协方差改进技巧,提出回归系数的参数型Bayes和经验Bayes迭代估计序列.证明了Bayes迭代估计的协方差矩阵序列的单调收敛性和Bayes迭代估计序列的一致性.当误差的协方差矩阵未知时,在均方误差准则(MSE)下,证明了经验Bayes迭代估计相对于单个方程的Bayes估计的优越性.这些结果进一步表明了协方差改进方法的有效性. 相似文献
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大维数据给传统的协方差阵估计方法带来了巨大的挑战,数据维度和噪声的影响不容忽视.首先以风险因子为自变量,对股票收益率建立线性回归模型;然后通过引入惩罚函数将取值非常接近的回归系数归为一组,近而来估计大维数据的协方差阵,提出了基于回归聚类算法的分块模型(BM-CAR),模型克服了传统的稀疏协方差阵估计的弊端.通过模拟和实证研究发现:较因子协方差阵估计方法而言,BM-CAR明显提高了大维协方差阵的估计效率;并且将其应用在投资组合时,投资者获得了更高的收益和经济福利. 相似文献
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本文利用密度的混合偏导数的核估计,构造出线性模型中误差协方差阵的逆的经验Bayes(EB)估计,在一定条件下,还证明了EB估计的收敛速度可任意接近于1,最后,给出了一个实例。 相似文献
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本文研究了多元线性同归模型岭估计的影响分析问题.利用最小二乘估计方法,获得了多元协方差阵扰动模型与原模型参数阵之间的岭估计的一些关系式,给出了度量影响大小的基于岭估计的广义Cook距离. 相似文献
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多元t分布下相依回归模型参数的两步估计 总被引:2,自引:0,他引:2
本文把文献中关于正态分布下相依回归模型参数Zellner估计的有限样本均方误差结果和效率结果以及两步协方差改进估计的一般均方误差结果推广到多元t分布情况,在该分布下两种估计的统计优效性质均不变. 相似文献
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该文在一般线性混合模型中, 研究了固定和随机效应线性组合的估计问题.对观测向量的协方差阵可以为奇异矩阵情形下,导出了该组合的最佳线性无偏估计,并证明了它的唯一性.在一般线性混合模型的特例, 三个小域模型下, 得到了小域均值ui 和方差分量的谱分解估计. 进而, 获得了基于谱分解估计的两步估计均方误差的二阶逼近. 相似文献
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Junichi Hirukawa 《Statistical Inference for Stochastic Processes》2017,20(3):329-346
Time series linear regression models with stationary residuals are a well studied topic, and have been widely applied in a number of fields. However, the stationarity assumption on the residuals seems to be restrictive. The analysis of relatively long stretches of time series data that may contain changes in the spectrum is of interest in many areas. Locally stationary processes have time-varying spectral densities, the structure of which smoothly changes in time. Therefore, we extend the model to the case of locally stationary residuals. The best linear unbiased estimator (BLUE) of vector of regression coefficients involves the residual covariance matrix which is usually unknown. Hence, we often use the least squares estimator (LSE), which is always feasible, but in general is not efficient. We evaluate the asymptotic covariance matrices of the BLUE and the LSE. We also study the efficiency of the LSE relative to the BLUE. Numerical examples illustrate the situation under locally stationary disturbances. 相似文献
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《Comptes Rendus de l'Academie des Sciences Series IIA Earth and Planetary Science》1998,326(4):495-498
Using an approach based on linear conditional expectations, we define a class of weak ARMA-GARCH representations. The generality of the class is illustrated through a collection of examples. We propose a two-stage estimation procedure based on the minimization of sums of squared linear predictions errors. Strong consistency and asymptotic normality of the estimator are established under ergodic and mixing assumptions. 相似文献
16.
Francisco Cribari-Neto Maria da Glória A. Lima 《Annals of the Institute of Statistical Mathematics》2010,62(6):1053-1082
The linear regression model is commonly used by practitioners to model the relationship between the variable of interest and
a set of explanatory variables. The assumption that all error variances are the same, known as homoskedasticity, is oftentimes
violated when cross sectional data are used. Consistent standard errors for the ordinary least squares estimators of the regression
parameters can be computed following the approach proposed by White (Econometrica 48:817–838, 1980). Such standard errors,
however, are considerably biased in samples of typical sizes. An improved covariance matrix estimator was proposed by Qian
and Wang (J Stat Comput Simul 70:161–174, 2001). In this paper, we improve upon the Qian–Wang estimator by defining a sequence
of bias-adjusted estimators with increasing accuracy. The numerical results show that the Qian–Wang estimator is typically
much less biased than the estimator proposed by Halbert White and that our correction to the former can be quite effective
in small samples. Finally, we show that the Qian–Wang estimator can be generalized into a broad class of heteroskedasticity-consistent
covariance matrix estimators, and our results can be easily extended to such a class of estimators. 相似文献
17.
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
18.
Li-Li Zhang 《Journal of Optimization Theory and Applications》2014,160(1):189-203
The matrix multisplitting iteration method is an effective tool for solving large sparse linear complementarity problems. However, at each iteration step we have to solve a sequence of linear complementarity sub-problems exactly. In this paper, we present a two-stage multisplitting iteration method, in which the modulus-based matrix splitting iteration and its relaxed variants are employed as inner iterations to solve the linear complementarity sub-problems approximately. The convergence theorems of these two-stage multisplitting iteration methods are established. Numerical experiments show that the two-stage multisplitting relaxation methods are superior to the matrix multisplitting iteration methods in computing time, and can achieve a satisfactory parallel efficiency. 相似文献
19.
Yan-meng Zhao Jin-hong You Yong Zhou 《应用数学学报(英文版)》2006,22(4):565-574
A partially linear regression model with heteroscedastic and/or serially correlated errors is studied here. It is well known that in order to apply the semiparametric least squares estimation (SLSE) to make statistical inference a consistent estimator of the asymptotic covariance matrix is needed. The traditional residual-based estimator of the asymptotic covariance matrix is not consistent when the errors are heteroscedastic and/or serially correlated. In this paper we propose a new estimator by truncating, which is an extension of the procedure in White. This estimator is shown to be consistent when the truncating parameter converges to infinity with some rate. 相似文献
20.
Mario Faliva 《Journal of multivariate analysis》2006,97(3):575-585
New results in matrix algebra applied to the fundamental bordered matrix of linear estimation theory pave the way towards obtaining additional and informative closed-form expressions for the best linear unbiased estimator (BLUE). The results prove significant in several respects. Indeed, more light is shed on the BLUE structure and on the working of the OLS estimator under nonsphericalness in (possibly) singular models. 相似文献