Abstract
With advancements in technology and data storage, the availability of functional data whose sample observations are recorded over a continuum, such as time, wavelength, space grids, and depth, progressively increases in almost all scientific branches. The functional linear regression models, including scalar-on-function and function-on-function, have become popular tools for exploring the functional relationships between the scalar response-functional predictors and functional response-functional predictors, respectively. However, most existing estimation strategies are based on non-robust estimators that are seriously hindered by outlying observations, which are common in applied research. In the case of outliers, the non-robust methods lead to undesirable estimation and prediction results. Using a readily-available R package robflreg, this paper presents several robust methods build upon the functional principal component analysis for modeling and predicting scalar-on-function and function-on-function regression models in the presence of outliers. The methods are demonstrated via simulated and empirical datasets.
Original language | English |
---|---|
Pages (from-to) | 212-233 |
Number of pages | 22 |
Journal | R Journal |
Volume | 15 |
Issue number | 1 |
DOIs | |
Publication status | Published - Mar 2023 |
Keywords
- Functional linear regression models
- Function-on-function linear regression model
- Functional principal component analysis
- Robust estimation of the scalar-on-function linear regression model
- Robust estimation of the function-on-function linear regression model