We propose a method which uses functional singular component to establish functional additive models. The proposed methodology reduces the curve regression problem to ordinary(i.e., scalar) additive regression problem...We propose a method which uses functional singular component to establish functional additive models. The proposed methodology reduces the curve regression problem to ordinary(i.e., scalar) additive regression problems of the singular components of the predictor process and response process. Consistency of estimators for the nonparametric function and prediction are proved, respectively. A simulation study is conducted to investigate the finite sample performances of the proposed estimators.展开更多
基金supported by National Natural Science Foundation of China (Grant Nos. 11171331, 11561006, 11331011)Program for Creative Research Group of National Natural Science Foundation of China (Grant No. 61621003)+4 种基金a Grant from the Key Lab of Random Complex Structure and Data Science, Chinese Academy of Sciencesthe Natural Science Foundation of Shenzhen UniversityResearch Projects of Colleges and Universities in Guangxi (Grant No. KY2015YB171)Innovation Project of Guangxi Graduate Education (Grant No. JGY2015122)a Grant from the Key Base of Humanities and Social Sciences in Guangxi College
文摘We propose a method which uses functional singular component to establish functional additive models. The proposed methodology reduces the curve regression problem to ordinary(i.e., scalar) additive regression problems of the singular components of the predictor process and response process. Consistency of estimators for the nonparametric function and prediction are proved, respectively. A simulation study is conducted to investigate the finite sample performances of the proposed estimators.
