论文标题
随时间不断发展的群集数据的估算方程式的渐近结果
Asymptotic results with estimating equations for time-evolving clustered data
论文作者
论文摘要
我们研究了从估计功能获得的估计量的存在，强大的一致性和渐近正态性，即p维的martingale变换。该问题是由进化群集数据的分析所激发的，分布属于指数家族，并且可能在其他组件序列方面有所不同。在准类方法中，我们构建了估计方程，该方程在响应载体的组成部分之间适应不同形式的依赖性，并在线性和广义线性模型上建立了与随机协变量的线性和广义线性模型的多变量扩展。此外，我们表征了渐近最佳的估计函数，因为它们导致置信区域的回归参数，该回归参数均非最小尺寸。包括模拟研究和对真实数据集的应用的结果。
We study the existence, strong consistency and asymptotic normality of estimators obtained from estimating functions, that are p-dimensional martingale transforms. The problem is motivated by the analysis of evolutionary clustered data, with distributions belonging to the exponential family, and which may also vary in terms of other component series. Within a quasi-likelihood approach, we construct estimating equations, which accommodate different forms of dependency among the components of the response vector and establish multivariate extensions of results on linear and generalized linear models, with stochastic covariates. Furthermore, we characterize estimating functions which are asymptotically optimal, in that they lead to confidence regions for the regression parameters which are of minimum size, asymptotically. Results from a simulation study and an application to a real dataset are included.
