随机效应生长曲线模型的一个注记(英文)

A Note on Random Effects Growth Curve Models

讨论回归系数估计的稳健性一直是回归分析中的一个热门话题.对于含随机效应的生长曲线模型,由于其响应变量观测之间不独立使得该问题的讨论异常困难,特别是当其设计矩阵非满秩时.本文不仅给出了当设计阵非满秩时广义最小二乘估计等于最佳线性无偏估计的充要条件,而且还在误差协差阵为任意正定阵的一般假设下给出了广义最小二乘估计与极大似然估计相等的充要条件.利用这些结论我们得到了在几种常见协差阵假定下广义最小二乘估计与极大似然估计相等的推论.文章最后还分别在设计阵满秩和非满秩情形下对所得理论结果进行了模拟演示.

The robustness of regression coefficient estimator is a hot topic in regression analysis all the while. Since the response observations are not independent, it is extraordinarily difficult to study this problem for random effects growth curve models, especially when the design matrix is non-full of rank. The paper not only gives the necessary and sufficient conditions under which the generalized least square estimate is identical to the the best linear unbiased estimate when error covariance matrix is an arbitrary positive definite matrix, but also obtains a concise condition under which the generalized least square estimate is identical to the maximum likelihood estimate when the design matrix is full or non-full of rank respectively. In addition, by using of the obtained results~ we get some corollaries for the the generalized least square estimate be equal to the maximum likelihood estimate under several common error covariance matrix assumptions. Illustrative examples for the case that the design matrix is full or non-full of rank are also given.