Estimating class‐specific parametric models under class uncertainty: local polynomial regression clustering in an hedonic analysis of wine markets
提出一种方法，在类别归属不确定时估计多个类别的回归模型，先通过局部多项式回归估计非参数模型，再聚类识别类别，最后估计各类别的参数函数，并应用于葡萄酒特征价格模型，识别出四类葡萄酒。
Abstract We introduce a method for estimating multiple class regression models when class membership is uncertain. The procedure— local polynomial regression clustering —first estimates a nonparametric model via local polynomial regression, and then identifies the underlying classes by aggregating sample observations into data clusters with similar estimates of the (local) functional relationships between dependent and independent variables. Finally, parametric functions specific to each class are estimated. The technique is applied to the estimation of a multiple‐class hedonic model for wine, resulting in the identification of four distinct wine classes based on differences in implicit prices of the attributes. Copyright © 2009 John Wiley & Sons, Ltd.