Online Program

Friday, October 21
Knowledge
Community
Influence
Fri, Oct 21, 8:00 AM - 8:50 AM
Carolina Ballroom
Poster Session 2 and Continental Breakfast
Sponsored by Bank of America

Robust Estimation Techniques for Functional Regression Models (303434)

*Melody Denhere, University of Mary Washington 

The field of functional data analysis (FDA) is an increasingly active field of study in statistics. This has been attributed in part to the growing interest in the analysis of 'big data' and also the need to have robust techniques that can deal with data that is in the form of images. In this work, we discuss different estimation methods for functional regression models in the presence of outliers. The functional covariates and functional parameters of the models are approximated in a finite dimensional space generated by an appropriate basis. This approach reduces the functional model to a standard multiple model with highly collinear covariates and potentially high dimensionality issues. The proposed estimator tackles these issues and also minimizes the effect of functional outliers. Results from a simulation study and a real world example are also presented to illustrate the performance of each proposed estimator.