  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.
