Ordering and Selecting Components in Multivariate or Functional Data Linear Prediction
多元或函数型数据线性预测中的成分排序与选择
Summary The problem of component choice in regression-based prediction has a long history. The main cases where important choices must be made are functional data analysis, and problems in which the explanatory variables are relatively high dimensional vectors. Indeed, principal component analysis has become the basis for methods for functional linear regression. In this context the number of components can also be interpreted as a smoothing parameter, and so the viewpoint…