This paper studies local polynomial estimation of expectile regression. Expectiles and quantiles both provide a full characterization of a (conditional) distribution function, but have each their own merits and inconveniences. Local polynomial fitting as a smoothing technique has a major advantage of being simple, allowing for explicit expressions and henceforth advantages when doing inference theory. The aim of this paper is twofold: to study in detail the use of local polynomial fitting in the context of expectile regression and to contribute to the important issue of bandwidth selection, from theoretical and practical points of view. We discuss local polynomial expectile regression estimators and establish an asymptotic normality result for them. The finite-sample performance of the estimators, combined with various bandwidth selectors, is investigated in a simulation study. Some illustrations with real data examples are given.

本文研究了期望回归的局部多项式估计。期望词和分位数都提供（条件）分布函数的完整特征，但各有优缺点。局部多项式拟合作为一种平滑技术，其主要优点是简单，允许使用显式表达式，从而在进行推论时具有优势。本文的目的是双重的：从理论和实践的角度，详细研究在期望回归中使用局部多项式拟合，并为带宽选择这一重要问题做出贡献。我们讨论了局部多项式期望回归估计量，并为它们建立了渐近正态性结果。估算器的有限样本性能，结合各种带宽选择器，在模拟研究中进行了研究。给出了一些带有真实数据示例的图示。