关于谱分解估计和方差分析估计在线性混合模型中的比较

在文献[1]中提出的谱分解估计是一种在线性混合模型中同时估计固定效应和方差分量的新方法.在本文中,我们对带有两个方差分量的线性混合模型进行了谱分解估计和方差分析估计的比较.得出了方差分量的这两种估计在某些条件下方差相等,而且谱分解估计具有一些方差分析估计的最优性.     

分块混合线性模型的参数估计

给出含有两个方差分量的分块混合线性模型,在一定条件下讨论给出三种模型的固定效应之间的关系,以及方差分量σ21和σ2的极大似然估计。     

线性混合模型中方差分量的估计的改进

线性混合模型是一类有着广泛应用的统计模型,对其中的方差分量,常使用方差分析法来估计。本文研究了在一种特殊情况下,含三个方差分量的线性混合随机效应模型的ANOVA估计,讨论了在何种条件下此估计在均方损失下一致优于ANOVA估计。由于此方差分析估计取负值的概率大于零,用在某非负点截尾的方法给出了方差分量的非负估计,并给出了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。     

线性混合效应模型中方差分量两种估计的比较

本文首先给出了由王松桂等提出的广义谱分解估计(GSDE)的定义,以及由Herderson方法得到的方差分量的方差分析估计(ANOVAE),在此基础上提出了谱和线性混合效应模型的概念,证明了在这类模型中,方差分析估计是广义谱分解估计的一种,并且考察了在一定条件下广义谱分解估计优于方差分析估计的充分条件.     

对混合效应模型中参数估计的一个思考

将含有两个随机效应的混合模型,借助奇异值分解,等价转换成两个新模型,给出了一个固定效应的无偏估计,并指出在满足一定条件下其方差小于最小二乘估计的方差,同时证明了新模型中参数的最小二乘估计等于原模型参数的方差分析估计。     

方差分量模型参数估计问题研究

方差分量模型的随机效应的协方差为单位阵时<线性模型引论>已进行研究.把随机效应的协方差推广为正定阵进行研究.用最小范数二次无偏估计法给出方差分量的估计.     

基于相关数据下的纵向数据混合效应模型估计

本文基于NPME模型提出一个核线性混合效应模型(KLME)估计量。该方法是结合核方法与线性混合效应模型方法得到的。此方法中,组内相关被融合成一种"权",此估计量能够达到很好的渐进有效性,并且在有限样本时更加实用。     

二次损失下方差分量模型中回归系数的线性Minimax估计

考虑方差分量模型,对任一可估函数,在二次损失下得到了线性可估函数在齐次估计类中的唯一的线性Minimax估计.     

PC准则下方差分量模型参数的广义岭估计的优良性

方差分量模型中,随机效应向量为正态情形时,本文证明了PC准则下其参数的广义岭估计优于其广LSE。     

鞅差序列下部分线性模型中非参数分量的经验似然的置信区间

构造了固定设计且误差为鞅差序列的相依样本情形部分线性模型非参数分量的经验似然比统计量,证明了统计量的极限分布为X1^2,在此基础上构造了部分线性模型非参数分量的经验似然置信区间．     

WOMBAT A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood （REML）

WOMBAT is a software package for quantitative genetic analyses of continuous traits, fitting a linear, mixed model; estimates of covariance components and the resulting genetic parameters are obtained by restricted maximum likelihood. A wide range of models, comprising numerous traits, multiple fixed and random effects, selected genetic covariance structures, random regression models and reduced rank estimation are accommodated. WOMBAT employs up-to-date numerical and computational methods. Together with the use of efficient compilers, this generates fast executable programs, suitable for large scale analyses. Use of WOMBAT is illustrated for a bivariate analysis. The package consists of the executable program, available for LINUX and WINDOWS environments, manual and a set of worked example, and can be downloaded free of charge from http://agbu. une.edu.au/~kmeyer/wombat.html     

非线性模型中无信息方差和协方差分量Bayes估计

采用Bayes方法从无先验信息出发,得到了非线性模型中方差和协方差分量的估计(包含相关系数),最后通过实例解算,结果表明:非线性模型中方差和协方差分量的估计,与ρ的理论值-0.5偏差不大,当没有先验信息时,该方法是可行的.     

The Importance of Identifying Levels in Multilevel Analysis: An Illustration of the Effects of Ignoring the Top or Intermediate Levels in School Effectiveness Research

In this article, the effect of ignoring one or more levels of variation in hierarchical linear regression analysis is explored. A model with four hierarchical levels is used as a reference model. A distinction is made between ignoring top and intermediate levels. The effects of ignoring levels on the fixed and on the random parameters of different random intercept models are explored by means of a real data set. The results show that ignoring an important level causes an effect on specific fixed coefficients, variance components and their corresponding standard error. Therefore, ignoring an important level can lead to different research conclusions.     

线性混合模型中固定效应参数的几类估计

文中首先将随机效应不同的线性混合模型转化为满足假设的线性模型.在此基础上,根据不同的情况,对其中的固定效应参数作最小二乘估计;当模型在较强的复共线性时,相应的对其做岭估计,并比较最小二乘估计与岭估计之间的优劣;当其中的随机变量之间并不独立时,通过对模型的变换,求出参数的广义最小二乘估计.     

重复观测时线性结构关系EV模型的参数估计

考虑一元线性结构关系EV模型y=a+bx,Y=y+E,X=x+u,在测量误差u和ξ的方差不相等时,对未知参数a和b进行了估计,利用重复观测数据,构造出参a,b和误差的方差的估计量,并证明了它们具有相合性.     

Brief Research Report: Bayesian Versus REML Estimations With Noninformative Priors in Multilevel Single-Case Data

Abstract

Recently, researchers have used multilevel models for estimating intervention effects in single-case experiments that include replications across participants (e.g., multiple baseline designs) or for combining results across multiple single-case studies. Researchers estimating these multilevel models have primarily relied on restricted maximum likelihood (REML) techniques, but Bayesian approaches have also been suggested. The purpose of this Monte Carlo simulation study was to examine the impact of estimation method (REML versus Bayesian with noninformative priors) on the estimation of treatment effects (relative bias, root mean square error) and on the inferences about those effects (interval coverage) for autocorrelated multiple-baseline data. Simulated conditions varied with regard to the number of participants, series length, and distribution of the variance within and across participants. REML and Bayesian estimation led to estimates of the fixed effects that showed little to no bias but that differentially impacted the inferences about the fixed effects and the estimates of the variances. Implications for applied researchers and methodologists are discussed.     

MIXED LINEAR MODEL APPROACHES FOR ANALYZING GENETIC MODELS OF COMPLEX QUANTITATIVE TRAITS

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Mixed linear model approaches for analyzing genetic models of complex quantitative traits

New approaches based on general mixed linear models were presented for analyzing complex quantitative traits in animal models, seed models and QTL (quantitative trait locus) mapping models. Variances and covariances can be appropriately estimated by MINQUE (minimum norm quadratic unbiased estimation) approaches. Random genetic effects can be predicted without bias by LUP (linear unbiased prediction) or AUP (adjusted unbiased prediction) methods. Mixed-model based composite interval mapping (MCIM) methods are suitable for efficiently searching QTLs along the whole genome. Bayesian methods and Markov Chain Monte Carlo (MCMC) methods can be applied in analyzing parameters of random effects as well as their variances. Projects supported by NSFC (39670390, 39893350) and the NIH Grant GM32518     

关于小离差主成分在经济计量分析中的应用研究

本文分析了小离差主成分与线性回归的几何意义,研究了利用小离差主成分建立线性方程或方程组,解决经济计量模型中参数估计问题,并得到了比较满意的结果。这种方法还具有能确定多重共线性、计算相对拟合度、减少回归次数、综合利用样本资料信息等有点。