Approximate Quasi-likelihood Estimation in Models With Surrogate Predictors
研究了当部分预测变量存在测量误差时，如何利用方差函数中的估计参数进行拟似然估计，综述并扩展了四种基于小测量误差近似的方法，并建立了测量误差研究中数据集的分类体系。
Abstract We consider quasi-likelihood estimation with estimated parameters in the variance function when some of the predictors are measured with error. We review and extend four approaches to estimation in this problem, all of them based on small measurement error approximations. A taxonomy of the data sets likely to be available in measurement error studies is developed. An asymptotic theory based on this taxonomy is obtained and includes measurement error and Berkson error models as special cases.