Nonparametric Density and Regression Estimation
这篇综述用非技术语言介绍了非参数密度和回归估计方法，无需预设函数形式，并通过实例和数据实验展示其灵活性和易用性，对希望避免模型设定错误的实证研究者有参考价值。
We provide a nontechnical review of recent nonparametric methods for estimating density and regression functions. The methods we describe make it possible for a researcher to estimate a regression function or density without having to specify in advance a particular--and hence potentially misspecified functional form. We compare these methods to more popular parametric alternatives (such as OLS), illustrate their use in several applications, and demonstrate their flexibility with actual data and generated-data experiments. We show that these methods are intuitive and easily implemented, and in the appropriate context may provide an attractive alternative to “simpler” parametric methods.