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加权广义估计方程有效性的评估AI 解读

The inverse probability weighted generalized estimating equations approach (Robins et al. 1994; Robins et al. 1995), effectively removes bias and provides valid statistical inference for regression parameter estimation in marginal models when longitudinal data contain missing values. The validity of the weighted generalized estimating equations regarding consistent estimation depends on whether the underlying missing data process is properly modelled. However, there is…

相关数据中信息性相关结构选择的模型诊断检验AI 解读

In the generalized method of moments approach to longitudinal data analysis, unbiased estimating functions can be constructed to incorporate both the marginal mean and the correlation structure of the data. Increasing the number of parameters in the correlation structure corresponds to increasing the number of estimating functions. Thus, building a correlation model is equivalent to selecting estimating functions. This paper proposes a chi-squared test to choose informative…

广义估计方程与二次推断函数的稳健性评估AI 解读

In the presence of data contamination or outliers, some empirical studies have indicated that the two methods of generalised estimating equations and quadratic inference functions appear to have rather different robustness behaviour. This paper presents a theoretical investigation from the perspective of the influence function to identify the causes for the difference. We show that quadratic inference functions lead to bounded influence functions and the corresponding…

纵向数据估计方程方法中可忽略缺失的检验AI 解读

We address the matter of determining whether or not missing data in longitudinal studies are ignorable with regard to quasilikelihood or estimating equations approaches. This involves testing for whether or not the zero‐mean property of estimating equations holds true. Chen & Little (1999) proposed testing for significant differences among parameter estimators calculated from sample subsets with different patterns of missing data, whereas we propose a more unified…

利用二次推断函数改进广义估计方程AI 解读

Generalised estimating equations enable one to estimate regression parameters consistently in longitudinal data analysis even when the correlation structure is misspecified. However, under such misspecification, the estimator of the regression parameter can be inefficient. In this paper we introduce a method of quadratic inference functions that does not involve direct estimation of the correlation parameter, and that remains optimal even if the working correlation structure…