Nonparametric Estimation of Regression Functions in the Presence of Irrelevant Regressors
考虑同时包含连续和离散解释变量的非参数回归模型，其中部分变量可能是冗余的。研究表明，数据驱动的最小二乘交叉验证方法可以渐近地剔除无关变量，且模拟显示该自动降维在有限样本中非常有效。
In this paper we consider a nonparametric regression model that admits a mix of continuous and discrete regressors, some of which may in fact be redundant (that is, irrelevant). We show that, asymptotically, a data-driven least squares cross-validation method can remove irrelevant regressors. Simulations reveal that this "automatic dimensionality reduction" feature is very effective in finite-sample settings. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.