首页|Model-averaging-based semiparametric modeling for conditional quantile prediction
Model-averaging-based semiparametric modeling for conditional quantile prediction
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
In real data analysis,the underlying model is frequently unknown.Hence,the modeling strategy plays a key role in the success of data analysis.Inspired by the idea of model averaging,we propose a novel semiparametric modeling strategy for the conditional quantile prediction,without assuming that the underlying model is any specific parametric or semiparametric model.Due to the optimality of the weights selected by leave-one-out cross-validation,the proposed modeling strategy provides a more precise prediction than those based on some commonly used semiparametric models such as the varying coefficient and additive models.Asymptotic properties are established in the proposed modeling strategy along with its estimation procedure.We conducted extensive simulations to compare our method with alternatives across various scenarios.The results show that our method provides more accurate predictions.Finally,we applied our approach to the Boston housing data,yielding more precise quantile predictions of house prices compared with commonly used methods,and thus offering a clearer picture of the Boston housing market.
asymptotic optimalityconditional quantile predictionkernel smoothingleave-one-out cross-validationmodel averagingvarying coefficient model
Chaohui Guo、Wenyang Zhang
展开 >
School of Mathematical Sciences,Chongqing Normal University,Chongqing 401331,China
Department of Mathematics,University of York,York YO10 5DD,UK
万方数据
