缺失数据下双重广义线性模型的参数估计

Parameters Estimation of Double Generalized Linear Models with Missing Data

在经济领域和工业产品质量改进试验中,对均值和散度同时建模十分必要;在数据采集过程中,时常会遇到数据缺失问题.文章基于上述两点,研究缺失数据下的双重广义线性模型的参数估计,采用最近距离插补和反距离加权插补对缺失数据进行处理,并应用最大扩展拟似然估计和最大伪似然估计两种估计方法对未知参数进行估计.随机模拟和实例结果表明,该模型和所应用的方法是有用和有效的.

In the econometric area and industrial quality improvement experiments,there is a great need to model the mean and dispersion simultaneously.In the process of data collection,we often encounter the problem of missing data.Based on the above two points,we investigate the estimation of mean parameters and dispersion parameters of double generalized linear models with missing data.Using interpolation methods of closest distance and inverse distance weighted,we estimated the unknown parameters using maximum extended quasilikelihood estimation and maximum pseudo-likelihood estimation.Simulation studies and a real example show that this model and methods are useful and effective.