纵向非单调缺失数据下部分线性模型的广义经验似然推断

摘要

To study the estimation in partially linear models for longitudinal with non-monotone missing data, based on quadratic inference functions, the generalized empirical likelihood method is used to estimate the regression coefficients and the baseline function, and the corresponding maximum empirical likelihood estimators are derived. The empirical log-likelihood ratios are proven to be asymptotically chi-squared, and the corresponding confidence regions and intervals are then constructed. The numerical study is conducted to compare the finite sample behavior of the generalized empirical likelihood and the normal approximation-based method.%为了研究纵向非单调缺失数据下部分线性模型的估计问题，基于二次推断函数提出了回归系数和基准函数的广义经验似然比函数，得到了相应的极大经验似然估计。证明了所提出的经验对数似然比渐近于卡方分布，由此构造了相应的置信域和逐点置信区间，模拟研究比较了广义经验似然与正态逼近方法的有限样本性质。