论文标题
模型基于有条件分位数预测的基于平均的半参数建模
Model Averaging based Semiparametric Modelling for Conditional Quantile Prediction
论文作者
论文摘要
在实际数据分析中，基础模型通常是未知的，建模策略在数据分析的成功中起着关键作用。由于模型平均的思想刺激，我们提出了一种新型的有条件分位数预测的半参数建模策略，而没有假设基础模型是任何特定的参数或半摩托模型。感谢通过交叉验证选择所选权重的最佳性，所提出的建模策略比基于一些常用的半摩托模型（例如变化的系数模型和添加剂模型）更准确地预测。提出的建模策略及其估计程序建立了渐近性能。进行了密集的仿真研究，以证明该方法与在各种情况下的替代方案相比的工作状况。结果表明，所提出的方法确实导致了比其替代方案更准确的预测。最后，提出的建模策略及其预测程序应用于波士顿住房数据，这比基于某些常用的替代方法更准确地预测了房价的分位数，因此，向我们介绍了波士顿房屋市场的更准确图片。
In real data analysis, the underlying model is usually unknown, modelling strategy plays a key role in the success of data analysis. Stimulated by the idea of model averaging, we propose a novel semiparametric modelling strategy for conditional quantile prediction, without assuming the underlying model is any specific parametric or semiparametric model. Thanks the optimality of the selected weights by cross-validation, the proposed modelling strategy results in a more accurate prediction than that based on some commonly used semiparametric models, such as the varying coefficient models and additive models. Asymptotic properties are established of the proposed modelling strategy together with its estimation procedure. Intensive simulation studies are conducted to demonstrate how well the proposed method works, compared with its alternatives under various circumstances. The results show the proposed method indeed leads to more accurate predictions than its alternatives. Finally, the proposed modelling strategy together with its prediction procedure are applied to the Boston housing data, which result in more accurate predictions of the quantiles of the house prices than that based on some commonly used alternative methods, therefore, present us a more accurate picture of the housing market in Boston.
