PLS Regression with Functional Predictor and Missing Data

摘要：

Time-average approximation and principal component analysis of the stochastic process underlying the functional data are the main ingredients for adapting NIPALS algorithm to estimate missing data in the functional context. The influence of the amount of missing data in the estimation of linear regression models is studied using the PLS method. A simulation study illustrates our methodology.

关键词： functional data missing data PLS functional regression models

作者: Cristian Preda Gilbert Saporta M.H. Ben Hadj Mbarek

作者单位: University des Sciences et Technologies de Lille 59650 Villeneuve dAscq, France Chaire de Statistique appliquee, CEDRIC, CNAM, 292 Rue Saint Martin, 75141 Paris Cedex 03, France Institut Superieur de Gestion de Sousse, Tunisie

会议类型: 国际会议

会议名称: The 6th International Conference on Partial Least Squares and Related Methods(第六届偏最小二乘及相关方法国际会议)

会议地点: 北京

会议语种:英文

页码: 17-22

在线出版日期: 2009-09-04（万方平台首次上网日期，不代表论文的发表时间）