Beaty T H, Self S G, Liang K Y, Connolly M A, Chase G A, Kwiterovich P O
Ann Hum Genet. 1985 Oct;49(4):315-28. doi: 10.1111/j.1469-1809.1985.tb01707.x.
A robust approach for analysis of variance components models is presented which does not rely on the assumption of multivariate normality for its validity. This approach uses the multivariated normal distribution as a 'working model' but obtains standard errors for the final estimators which do not depend on this underlying distribution. By using the observed variance in the first derivatives of the multivariate normal 'working model' to modify the conventional score test, hypotheses regarding specific components can also be tested without relying directly on the assumption of multivariate normality. A special case is presented where both the modified score test and the likelihood ratio test are equally robust, and simulated data are used to illustrate this situation. Measurements of triglyceride levels in 391 individuals in 60 families randomly selected from the membership of a health maintenance organization are used to illustrate this robust approach to variance components.
提出了一种用于方差分量模型分析的稳健方法，该方法的有效性不依赖于多元正态性假设。此方法将多元正态分布用作“工作模型”，但为最终估计量获得的标准误差并不依赖于这一基础分布。通过利用多元正态“工作模型”一阶导数中的观测方差来修正传统得分检验，也可以在不直接依赖多元正态性假设的情况下检验关于特定分量的假设。给出了一个特殊情况，其中修正得分检验和似然比检验同样稳健，并使用模拟数据来说明这种情况。从一家健康维护组织的成员中随机选取60个家庭的391名个体的甘油三酯水平测量值，用于说明这种稳健的方差分量方法。
