Weighted least squares methods for prediction in the functional data linear model

Details Weighted least squares methods for prediction in the functional data linear model Weighted least squares methods for prediction in the functional data linear model

2009-02-19

arxiv.org/abs/0902.3319v1

Statistics

Methodology

Submitted to the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics

Scientific paper

The problem of prediction in functional linear regression is conventionally addressed by reducing dimension via the standard principal component basis. In this paper we show that an alternative basis chosen through weighted least-squares, or weighted least-squares itself, can be more effective when the experimental errors are heteroscedastic. We give a concise theoretical result which demonstrates the effectiveness of this approach, even when the model for the variance is inaccurate, and we explore the numerical properties of the method. We show too that the advantages of the suggested adaptive techniques are not found only in low-dimensional aspects of the problem; rather, they accrue almost equally among all dimensions.

Affiliated with

Also associated with

No associations

Rating

Weighted least squares methods for prediction in the functional data linear model does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Weighted least squares methods for prediction in the functional data linear model, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Weighted least squares methods for prediction in the functional data linear model will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-260181