线性混合效应模型中随机效应密度的非参数估计

对于Tao等(1999)提出的一类线性混合效应模型,构造了随机效应密度的非参数估计,并证明了此估计量具有渐近无偏性.     

随机效应模型中方差分量的非负二次型估计

设Y_1,Y_2是相互独立的随机变量,Y_1/(ασ+τ)～X~2(n_1),Y_2/τ～x~2(n_2),其中σ>0,τ>0是未知方差分量,α>0,正整数n_1,n_2是已知常数。本文从风险函数及偏差角度研究了σ的无偏估计Y_1/(αn_1)—Y_2/(αn_2)的改进,并指出用非负二次估计PY_1代替σ的无偏估计较合适,其中最后把上述结果用于一种方式分组及二级套分类随机效应模型。     

正态线性模型中随机回归系数和参数的Minimax估计

该文在一般正态随机效应线性模型中研究了随机回归系数和参数的估计问题. 在二次损失下，得到了线性可估函数在一切估计类中的唯一Minimax估计.     

线性混合效应模型中方差分量的估计

本文首先研究了含三个方差分量的线性混合随机效应模型改进的ANOVA估计, 此估计在均方损失下一致优于ANOVA估计. 由于这些方差估计取负值的概率大于零, 对得到的估计在某非负点采用截尾的方法得到非负估计是一种常用的方法. 对文章中提出的估计, 研究了此估计在某非负点截尾之后得到的估计在均方损失意义下优于截尾之前的估计的充分条件, 同时给出ANOVA估计在截尾之后优于它本身的充分条件, 而且将得到的结论推广到更一般的线性混合随机效应模型.     

部分线性模型的随机约束岭估计

研究了部分线性回归模型附加有随机约束条件时的估计问题.基于Profile最小二乘方法和混合估计方法提出了参数分量随机约束下的Profile混合估计,并研究了其性质.为了克服共线性问题,构造了参数分量的Profile混合岭估计,并给出了估计量的偏和方差.     

多元线性模型中随机回归系数和参数的最优估计

本文研究了一般的随机效应多元线性模型中线性可估函数的最优线性无偏估计。特别地 ,考虑了一类特殊的估计 :Φ-线性估计 ,给出了 Φ-线性可估函数和最优 Φ—线性无偏估计的定义。得到了 Φ-线性可估函数的最优Φ—线性无偏估计 ,并证明了它在几乎处处意义下的唯一性     

多元线性模型中随机回归系数和参数的线性估计的泛容许性

本文对于一般的随机效应多元线性模型，给出了随机回归系数和参数的线性可估函数的泛容许性估计的定义，并得到了随机回归系数和参数的线性可估函数的齐线性估计在齐线性估计类中泛容许性的特征。     

线性模型中M估计分布的随机加权方法逼近

在线性模型中,M估计的渐近分布通常都涉及到不易估计的未知误差分布的某些量,如果要估计渐近方差,就需对这些冗余参数进行估计.利用随机加权方法可以避免先对误差分布中的冗余参数进行估计.给出了当自变量是随机变量时,M估计分布的随机加权逼近,证明了M估计分布的随机加权逼近是一致相合的.当取不同的凸函数,样本大小和随机权时,进一步利用蒙特卡洛方法研究估计分布.研究表明随机权取泊松权时,不仅达到同样的效果而且可以减小计算量.     

广义线性模型参数的自适应拟似然估计

对广义线性模型参数的一种拟似然估计的理论给予了彻底的处理. 在该估计中,响应变量的未知的协方差阵是通过样本去估计的.证明了所定义的估计量具有下述意义上的渐近有效性：当样本量n→∞时, 该估计有渐近正态性,且其极限分布的协方差阵重合于当响应变量的协方差阵完全已知时,拟似然估计的极限分布的协方差阵.     

带插值条件的拟线性最小二乘估计

提出了带插值点的拟线性最小二乘法,并给出了带插值点的拟线性最小二乘法拟合的最小二乘估计,证明了及其参数具有无偏性。     

线性混合模型中方差分量的ANOVA估计的改进

讨论了在含三个方差分量的线性混合模型中,在均方误差意义下,方差分量的方差分析估计的改进,并把这一结果推广到一般的线性混合模型上,得到一个改进方差分析估计的简单方法.     

Monte Carlo approximation through Gibbs output in generalized linear mixed models

Geyer (J. Roy. Statist. Soc. 56 (1994) 291) proposed Monte Carlo method to approximate the whole likelihood function. His method is limited to choosing a proper reference point. We attempt to improve the method by assigning some prior information to the parameters and using the Gibbs output to evaluate the marginal likelihood and its derivatives through a Monte Carlo approximation. Vague priors are assigned to the parameters as well as the random effects within the Bayesian framework to represent a non-informative setting. Then the maximum likelihood estimates are obtained through the Newton Raphson method. Thus, out method serves as a bridge between Bayesian and classical approaches. The method is illustrated by analyzing the famous salamander mating data by generalized linear mixed models.     

线性混合模型方差分量的检验

对于含有两个方差分量的随机效应设计阵为任意阵的线性混合模型的方差分量单边检验问题给出了精确的F检验和基于广义p值的检验.对于给出的精确的F检验给出检验存在条件以及是一致最优无偏检验的条件.通过数值模拟,基于广义p值检验的功效和犯第一类错误的概率被讨论,由模拟结果可以看出基于广义p值的检验很好地控制了犯第一类错误的概率.     

Checking for normality in linear mixed models

Linear mixed models are popularly used to fit continuous longitudinal data,and the random effects are commonly assumed to have normal distribution.However,this assumption needs to be tested so that further analysis can be proceeded well.In this paper,we consider the Baringhaus-Henze-Epps-Pulley (BHEP) tests,which are based on an empirical characteristic function.Differing from their case,we consider the normality checking for the random effects which are unobservable and the test should be based on their predictors.The test is consistent against global alternatives,and is sensitive to the local alternatives converging to the null at a certain rate arbitrarily close to 1/√ n where n is sample size.Furthermore,to overcome the problem that the limiting null distribution of the test is not tractable,we suggest a new method: use a conditional Monte Carlo test (CMCT) to approximate the null distribution,and then to simulate p-values.The test is compared with existing methods,the power is examined,and several examples are applied to illustrate the usefulness of our test in the analysis of longitudinal data.     

Bayesian estimation of the intraclass correlation coefficients in the mixed linear model

The method of determining Bayesian estimators for the special ratios of variance components called the intraclass correlation coefficients is presented. The exact posterior distribution for these ratios of variance components is obtained. The approximate posterior mean of this distribution is also derived. All computations are non-iterative and avoid numerical integration.     

基于EM算法的混合自回归滑动平均模型的参数估计

研究了一类用于时间序列建模的混合自回归滑动平均模型.该模型是由m个ARMA分量经过混合得到的,给出了混合自回归滑动平均模型参数估计的期望极大化(EM)算法,从而得到了混合系数和分量模型的参数,通过仿真说明了其有效性.     

Bayesian hierarchical linear mixed models for additive smoothing splines

Bayesian hierarchical models have been used for smoothing splines, thin-plate splines, and L-splines. In analyzing high dimensional data sets, additive models and backfitting methods are often used. A full Bayesian analysis for such models may include a large number of random effects, many of which are not intuitive, so researchers typically use noninformative improper or nearly improper priors. We investigate propriety of the posterior for these cases. Our findings extend known results for normal linear mixed models to certain cases with Bayesian additive smoothing spline models. Supported by National Science Foundation grant SES-0351523 and by National Institutes of Health grants R01-CA100760 and R01-MH071418.     

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.     

Quasi-Monte Carlo methods for integration of functions with dominating mixed smoothness in arbitrary dimension

In a celebrated construction, Chen and Skriganov gave explicit examples of point sets achieving the best possible _L2$L_2$-norm of the discrepancy function. We consider the discrepancy function of the Chen–Skriganov point sets in Besov spaces with dominating mixed smoothness and show that they also achieve the best possible rate in this setting. The proof uses a b$b$-adic generalization of the Haar system and corresponding characterizations of the Besov space norm. Results for further function spaces and integration errors are concluded.     

A complete class for linear estimation in a general linear model

Summary It is shown that in linear estimation both unbiased and biased, all unique (up to equivalence with respect to risk) locally best estimators and their limits constitute a complete class.