线性模型的泛线性假设检验

在多元线性模型的实际应用中,经常会遇到一类线性模型的泛线性假设的问题。 例1 两总体的比较问题。 设x_1,…x_n来自正态总体N(μ_1,∑),z_1,…,z_n自正态总体     

奇异线性模型的线性假设检验问题

本文研究奇异线性模型的假设检验问题，我们用初等直接的方法了根据最小二乘统一理论所构造的检验统计量服从Ｆ分布，并给出了这些结果在ｐａｎｅｌ数据模型。两级抽样回归模型以及不完全数据回归模型中的应用。     

线性矩阵方程与线性矩阵方程组

研究了线性矩阵方程(组)的解的结构理论和求解方法.     

线性模型参数M估计的线性表示

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。     

线性空间与二阶齐次线性递推数列

本文运用线性空间的理论与方法,建立了二阶齐次线性递推数列的通项公式.     

带约束线性模型中的可容许线性估计

带约束线性模型中的可容许线性估计张双林（黑龙江大学数学系，哈尔滨１５００８０）１．引言及主要结果考虑线性模型当参数不受约束时，Ｒａｏ［１］，吴启光［２］，朱显海和鹿长余［３］等给出了Ｓｎ×Ｐβ的线性估计在线性类中是可容许的充要条件，当参数在约束条件，...     

多维线性模型均值矩阵的线性Minimax估计

本研究多维线性模型中均值矩阵在不同损失函数下的线性Ｍｉｎｉｍａｘ估计,得到了具体的表达式。     

多元线性回归模型中的线性经验Bayes估计

H.Robbins提出的经验Bayes估计应用于多元线性回归模型,已有一些文献进行了讨论。Martz和Krutchkoff通过Monte-Carlo模拟试验讨论了一般假设下的线性回归模型问题,但并没有从理论上证明他们所给出的EB估计的任何相合性和渐近最优性。Wind考虑了当误差向量满足0均值σ~2I为协方差阵时,回归系数的EB估计问题,但其结果也存在着缺陷。Singh考虑了在多元线性回归模型中误差向量具有多元正     

线性偏微分方程的某些线性定解问题

引言在解决一系列自然科学和技术问题的时候,常常会遇到偏微分方程。我们讲义的目的就是要研究二階线性偏微分方程理论中的某些基本问题。从初等的数学物理教程中,我们已熟知下列简单的二階线性偏微分方程: 弦振动方程拉普拉(Laplace)方程和热传导方程其中u(x,y)是未知函数,而x,y是自变量(平面上的笛卡儿坐标)。     

两个线性模型间最优线性无偏预测的等价性

设M1和M2是两个带有预测量的线性模型,通过使用矩阵秩方法,本文给出了模型M1下预测量的最优线性无偏预测同时也是模型M2下的最优线性无偏预测的充分必要条件.作为这个结果的应用,我们给出了两个线性混合模型间最优线性无偏预测等价性的充分必要条件.     

不同混合线性模型下线性函数的最佳线性无偏预测相等的条件

考虑一个不仅对协方差矩阵没有任何秩假设,而且对随机效应向量和随机误差向量之间的关系没有任何限制的混合线性模型.给出了线性统计量Ay是线性函数f（L,N）的最佳线性无偏预测的充要条件;同时也给出了在混合线性模型M1下BLUP（f（L,N））仍是在混合线性模型M2下BLUP（f（L,N））的充要条件;最后给出在两混合线性模型下BLUP（f（L,N））相等的条件.     

Best linear unbiased prediction for linear combinations in general mixed linear models

The general mixed linear model can be written as . In this paper, we mainly deal with two problems. Firstly, the problem of predicting a general linear combination of fixed effects and realized values of random effects in a general mixed linear model is considered and an explicit representation of the best linear unbiased predictor (BLUP) is derived. In addition, we apply the resulting conclusion to several special models and offer an alternative to characterization of BLUP. Secondly, we recall the notion of linear sufficiency and consider it as regards the BLUP problem and characterize it in several different ways. Further, we study the concepts of linear sufficiency, linear minimal sufficiency and linear completeness, and give relations among them. Finally, four concluding remarks are given.     

秩亏线性模型中的一个新的线性有偏估计类

本文基于最优线性最小偏差估计的谱分解,定义了秩亏线性模型未知参数的一个新的线性有偏估计类,并讨论了它的许多重要性质,通过选取偏参数的适当形式,构造了许多很有意义的线性有偏估计,最后,给出了一个算例。     

多元线性模型中条件最优预测的稳健性

研究了多元线性模型中条件最优线性无偏预测的稳健性问题,得到了条件线性可预测变量的这种预测关于协方差矩阵具有稳健性的充要条件.     

On the equivalence of the BLUEs under a general linear model and its restricted and stochastically restricted models

Necessary and sufficient conditions are derived for the equalities of the best linear unbiased estimators (BLUEs) of parametric functions under a general linear model and its restricted and stochastically restricted models to hold.     

任意秩有限总体中无偏预测和估计的构造关系

研究了有限总体均值向量的无偏估计和线性可预测变量的无偏预测之间的关系,利用分块矩阵广义逆直接对加权风险函数进行分解,提出了一种由均值向量的无偏估计来构造无偏预测的新方法,并找到了它们之间的构造关系.特别地,线性可预测变量的最优线性无偏预测(BLUP)可由均值向量的最佳线性无偏估计(BLUE)惟一地表示(有关惟一性在几乎处处意义下理解).     

具有一般协方差结构线性模型的局部影响评价

该文讨论了具有一般协方差结构线性模型的局部影响分析问题. 通过对广义Cook统计量中M/c的适当选取, 文章给出了一种对扰动的参数变换具有不变性质的局部影响度量. 在具协方差扰动模式下, 该文给出了回归系数和方差系数估计、最佳线性预测的局部影响诊断统计量.该结果与数据删除法进行了比较, 并通过实例进行了分析和说明.     

Covariance tapering for prediction of large spatial data sets in transformed random fields

The best linear unbiased predictor (BLUP) is called a kriging predictor and has been widely used to interpolate a spatially correlated random process in scientific areas such as geostatistics. However, if an underlying random field is not Gaussian, the optimality of the BLUP in the mean squared error (MSE) sense is unclear because it is not always identical with the conditional expectation. Moreover, in many cases, data sets in spatial problems are often so large that a kriging predictor is impractically time-consuming. To reduce the computational complexity, covariance tapering has been developed for large spatial data sets. In this paper, we consider covariance tapering in a class of transformed Gaussian models for random fields and show that the BLUP using covariance tapering, the BLUP and the optimal predictor are asymptotically equivalent in the MSE sense if the underlying Gaussian random field has the Matérn covariance function.     

Prediction with measurement errors in finite populations

We address the problem of selecting the best linear unbiased predictor (BLUP) of the latent value (e.g., serum glucose fasting level) of sample subjects with heteroskedastic measurement errors. Using a simple example, we compare the usual mixed model BLUP to a similar predictor based on a mixed model framed in a finite population (FPMM) setup with two sources of variability, the first of which corresponds to simple random sampling and the second, to heteroskedastic measurement errors. Under this last approach, we show that when measurement errors are subject-specific, the BLUP shrinkage constants are based on a pooled measurement error variance as opposed to the individual ones generally considered for the usual mixed model BLUP. In contrast, when the heteroskedastic measurement errors are measurement condition-specific, the FPMM BLUP involves different shrinkage constants. We also show that in this setup, when measurement errors are subject-specific, the usual mixed model predictor is biased but has a smaller mean squared error than the FPMM BLUP which points to some difficulties in the interpretation of such predictors.