Modeling measurement error problems using generalized quasi-likelihood method

在对具有相关响应的数据进行统计建模时，协变量的测量误差常常被忽略，特别是在社会经济研究中收集的数据。这会导致模型参数的估计偏差，通常会导致推理效率不理想。这种估计偏差的修正最近引起了统计学家的注意。广义矩量法（GMM）已被经济学家广泛应用于依赖数据，以获得一致和有效的模型参数估计。与GMM和其他广义估计方程（GEE）方法相比，利用重复数据的均值向量和协方差结构的广义拟似然方法可以产生一致且更有效的估计。

In statistical modeling of data with dependent responses, measurement errors in covariates are often neglected, especially for data collected in socioeconomic studies. This causes estimation bias of the model parameters and usually leads to unsatisfactory efficiency of the inference. The correction of this estimation bias has recently attracted the attention of statisticians.The generalized method of moments (GMM) has been widely used to dependent data by economists to obtain consistent and efficient estimates of model parameters. It has be also demonstrated that the generalized quasilikelihood approach, which exploits the mean vector and the covariance structure of the repeated data, produces consistent and more efficient estimates compared with the GMM and other generalized estimating equations (GEE) methods.