Abstract
Establishing an optimal sampling schedule is a crucial step toward a precise inference of the underlying functional mechanism of a process, especially when data collection is expensive/difficult. This work is concerned with optimal sampling plans for predicting a scalar response using a functional predictor when a quadratic regression relationship is present. An optimality criterion for selecting the best sampling schedules is derived, and some important properties of the criterion are provided. In addition, a bootstrap aggregating (bagging) strategy is proposed to enhance the quality of the obtained sampling schedule.
Original language | English (US) |
---|---|
Article number | 91 |
Journal | Journal of Statistical Theory and Practice |
Volume | 15 |
Issue number | 4 |
DOIs | |
State | Published - Dec 2021 |
Keywords
- Bagging
- Functional data analysis
- Functional principal component
- Functional regression model
ASJC Scopus subject areas
- Statistics and Probability