Two-stage estimation and bias-corrected empirical likelihood in a partially linear single-index varying-coefficient model
研究了部分线性单指标变系数模型中参数的估计与经验似然方法，提出两阶段估计和偏差校正经验似然比，用于构造参数的置信区域。
Abstract The estimation and empirical likelihood (EL) of the parameters of interest in a partially linear single-index varying-coefficient model are studied. A two-stage method is presented to estimate the regression parameters and the coefficient functions. The asymptotic distributions of the proposed estimators are obtained. Meanwhile, a bias-corrected EL ratio for the regression parameters is proposed. It is shown that the ratio is asymptotically standard chi-squared. The result can be directly used to construct the EL confidence regions of the regression parameters. Simulation studies are carried out to evaluate the finite sample behaviour of the proposed method. An application example of a real data set is given.