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The confidence band of functions is complicated by the over-smoothing problem and the residual distribution. In this paper, we use bootstrap and data-sharpening methods to establish a general confidence band. The construction is simple and the band is narrower than existing estimation methods. At the same time, a technique based on quantiles makes the confidence band more controllable and damps down the stochastic error term. Afterwards, we conduct a limited simulation to illustrate that the proposed band performs better than existing ones. Finally, we show the theoretical properties of the results and prove them.

Data sharpening method in regression confidence band