In this study, we propose a model averaging approach to estimating the conditional quantiles based on a set of semiparametric varying coefficient models. Different from existing literature on the subject, we consider a particular form for all candidates, where there is only one varying coefficient in each sub-model, and all the candidates under investigation may be misspecified. We propose a weight choice criterion based on a leave-more-out cross-validation objective function. Moreover, the resulting averaging estimator is more robust against model misspecification due to the weighted coefficients that adjust the relative importance of the varying and constant coefficients for the same predictors. We prove out statistical properties for each sub-model and asymptotic optimality of the weight selection method. Simulation studies show that the proposed procedure has satisfactory prediction accuracy. An analysis of a skin cutaneous melanoma data further supports the merits of the proposed approach.

在这项研究中，我们提出了一种基于一组半参数变系数模型来估计条件分位数的模型平均方法。与有关该主题的现有文献不同，我们为所有候选者考虑一种特定形式，其中每个子模型中只有一个可变系数，并且所有被调查的候选者都可能被错误指定。我们提出了一种基于 leave-more-out 交叉验证目标函数的权重选择标准。此外，由于加权系数调整相同预测变量的变化系数和常数系数的相对重要性，因此生成的平均估计器更能抵抗模型错误指定。我们证明了每个子模型的统计特性和权重选择方法的渐近最优性。仿真研究表明，所提出的程序具有令人满意的预测精度。对皮肤黑色素瘤数据的分析进一步支持了所提出方法的优点。