随机效应

LM检验证明随机效应模型优于固定效应模型。

LM test proves the random effect model is superior to the fixed effect model .

总结了动态平行数据模型的固定效应与随机效应模型的最小二乘估计（OLS）与工具变量估计（Tool）方法。

In fixed effect model and random effect model of dynamic panel data , we summarized the OLS method with instrument tool method of parameter estimating .

指明按系统效应导致的分量与随机效应导致的分量的大小比，用B类方法计算相关系数的具体步骤。

It is also Pointed out that the system effect may result in size proportion of the component .

具有线性趋势回归信度模型的随机效应及线性趋势的Score检验

Score testing of random effect and linear trend for regression credibility model with linear trend

随机效应Logistic模型的参数估计

Parameters Estimation in Random Effects Logistic Model

非平衡随机效应模型中方差分量的经验Bayes估计

On Empirical Bayes Estimation of Variance Components in Unbalanced Random Effects Models

具有AR（1）误差的非线性随机效应模型中自相关系数的扰动诊断

Perturbation Diagnostics of Autocorrelation Coefficients in Nonlinear Models with Random Effects and AR ( 1 ) Errors

这时，比较常用的是随机效应Logistic模型。本文讨论了如何运用EM算法对随机效应Logistic模型进行参数估计。

In this paper , EM algorithms is used to estimate the parameters of random effects logistic model .

方法采用随机效应模型进行meta综合分析合并率差。

Methods Meta-analysis was applied with a random-effect model for the collected data .

具有AR（1）误差的线性随机效应模型中方差齐性和自相关性的检验

Testing of Homogeneity for Variance and Autocorrelation in Linear Models with AR ( 1 ) Errors and Random Effects

统计处理采用Meta分析的固定效应模型和随机效应模型。

Fixed-effect model ( FEM ) and random-effect model ( REM ) of Meta-analysis were applied to process data .

方法在分层贝叶斯框架下，推出随机效应模型meta分析参数的完全条件分布。

Methods The full conditional distributions of parameters in Gibbs sampling for meta analysis based on random effects model is derived .

计算结果表明，海温异常的随机效应是一个长周期过程，时间尺度约为140天，可以用实际测得的拖曳系数CD和海温起伏方差q~2值对它进行预测。

Calculations indicate that the measured drag coefficient CD and the OSTF variance q2 can be used to predict the stochastic effect of anomalous ocean surface temperature , which has duration of the order of 140 days .

导出了混合线性模型在各种扰动下效应参数的Bayes局部影响度量，并给出了平衡单向分类随机效应模型下的一些结果。

The Bayes local influence measures are derived under various perturbation schemes , and some results in random-effects model of one-way classification are obtained .

方法以云南锡业总公司劳动防护研究所提供的高危人群队列的随访资料为例，拟合3个水平的截断数据的多重成员Cox模型，并分析各水平的随机效应及其与肺癌发生间的关系。

Methods A 3-level multiple membership Cox model was fitted for the truncated cohort data among high risk population in Yunnan Tin Cooperation .

在MLM中，亲缘全系矩阵为随机效应。

In the compressed MLM , relative kinship matrix was a random effect .

对于遗传力较低而QTL方差贡献较大的性状，采用标记辅助遗传评定效果较好；将QTL效应作为随机效应，可以提高种畜效应值估计的效果。

The lower the heritability and the larger the QTL variance component of the trait , the more accuracy of the marker assisted evaluation .

随机效应的Meta分析模型涉及这样一个假定，在各个研究中的被估计效应是不一样的，但遵循某种分布。

A random effects meta-analysis model involves an assumption that the effects being estimated in the different studies are not identical , but follow some distribution .

从AR（p）-ARCH（q）模型出发，并结合KIC定阶方法，对随机效应进行预测。

Beginning with AR ( p ) - ARCH ( q ) models , as well as KIC method , confirm the order p , and predict the random effects .

注意在随机效应的Meta分析的可信区间不同研究间是不确定的，因为它们的平均效应是不同的；

Note that the confidence interval from a random effects meta-analysis describes uncertainty in the location of the mean of systematically different effects in the different studies .

刻画Poisson回归模型的偏大离差性的两种方法是拟似然方法和随机效应法（Lee＆Nelder，2000），已有许多作者利用随机效应法研究了Poisson模型的偏大离差的检验问题。

Two ways of modelling overdispersion in Poisson regression models are quasi-likelihood approach and random-effect approach . Many authors have discussed the tests for detecting overdispersion in Poisson models using random-effect methods .

该文利用非线性随机效应模型在欧氏空间的BW几何结构，求出了固定效应参数估计的渐近性质与随机展开式。

In this paper , the BW geometric framework for nonlinear models with random effects is utilized to derive random expansion of fixed effects parameter estimator .

局限在于，在实证分析中采用混同OLS估计方法，没有考虑面板数据的固定效应和随机效应，这将是进一步研究的方向。

The limitation is that the estimate procedure is pooled OLS , without considering the fixed effect and random effect in the panel data .

对单向随机效应模型的方差显著性检验给出了参数Bootstrap方法，该方法解决了广义p-值方法不能保证检验水平的问题。

A parametric bootstrap method to test the significance of random effects in one-way random effects model is given , this approach solves the problem of the generalized p-value method .

方法：应用Meta分析方法的随机效应模型，对国内2000~2004年采用中医治疗溃疡性结肠炎，并以西医治疗作对照的研究文献进行定量合并分析。

Methods : Randomized effect model of Meta-analysis was used for quantitatively combined analysis on research literature about simple TCM treatment of ulcerative colitis with controlled study of simple western medicine from 2000 ~ 2004 in China .

本文利用1995-19972000-2003年期间，中国工业36个行业的平行数据，使用固定效应模型和随机效应模型估计了FDI对工业行业内部的技术外溢，及影响技术外溢的因素。

Based on the panel data of a sample of 36 industry sectors during 1995-1997 and 2000-2003 , this paper estimates the technological spillover effect of FDI ( Foreign Direct Investment ) to domestic industry firms with fix effect model and random effect model .

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。

Consider a kind of linear mixed effects model which was proposed by Tao et al ( 1999 ), the nonparametric estimator of the density of the random effects was established . It is shown that the estimator is an asymptotic unbiased estimator .

对已发表的9篇关于纯母乳喂养降低婴儿腹泻的文献进行Meta分析，齐性检验表明，应选择随机效应模型进行加权合并，最终合并OR值为0.3458，其95%的可信区间为0.2297～0.5205。

In the present paper , date from 9 pertinent published studies on the breast feeding reducing infantile diarrhea were analyzed by means of Meta analysis . Homogeneity test showed that Random Effect Model should be selected for weighting and pooling data .

与普通最小二乘法（OLS）相比，可行广义最小二乘法（FGLS）对随机效应模型的估计更为有效。

Compared with ordinary least square method ( OLS ), the estimation of the random effect model of the feasible generalized least squares method ( FGLS ) is more effective .

首先通过数据包络方法（DEA）计算出各地的粮食生产技术效率，然后运用时间随机效应模型和可行广义最小二乘法（GeneralFGLS）分析估计三个因素对农业水资源压力指数的影响。

Firstly , the paper calculate the technical efficiency of food production by DEA , and then use the time random effects models and possible generalized least squares ( General FGLS ) to estimate the impact of three factors on agricultural water stress index .