缺失数据下广义线性模型的经验似然推断

Empirical Likelihood for Generalized Linear Models with Missing Data

利用经验似然方法,讨论缺失数据下广义线性模型中参数的置信域问题,得到了对数经验似然比统计量的渐近分布为标准卡方分布;给出参数的一些估计量及其渐近分布,利用数据模拟解释了所提出的方法。

Using the empirical likelihood method, the con[idence region of parameter in generalized linear model with missing data is considered. It is shown that the empirical log-likelihood ratio at the true parameter converges to the standard ehi-square distribution. Also, a class of estimators for the parameter are constructed, and the asymptotic distributions of the proposed estimators are obtained. Some simulations are conducted to illustrate the proposed method.