ASYMPTOTIC EQUIVALENCE OF FUNCTIONAL LINEAR REGRESSION AND A WHITE NOISE INVERSE PROBLEM

作者：Meister Alexander^*

来源：Annals of Statistics, 2011, 39(3): 1471-1495.

DOI：10.1214/10-AOS872

• Functional data analysis
• LeCam equivalence
• nonparametric statistics
• statistical inverse problems
• white noise model

We consider the statistical experiment of functional linear regression (FLR). Furthermore, we introduce a white noise model where one observes an Ito process, which contains the covariance operator of the corresponding FLR model in its construction. We prove asymptotic equivalence of FLR and this white noise model in LeCam's sense under known design distribution. Moreover, we show equivalence of FLR and an empirical version of the white noise model for finite sample sizes. As an application, we derive sharp minimax constants in the FLR model which are still valid in the case of unknown design distribution.

• 出版日期2011-6