Bagging-Enhanced Sampling Schedule for Functional Quadratic Regression

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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 languageEnglish (US)
Article number91
JournalJournal of Statistical Theory and Practice
Volume15
Issue number4
DOIs
StatePublished - Dec 2021

Keywords

  • Bagging
  • Functional data analysis
  • Functional principal component
  • Functional regression model

ASJC Scopus subject areas

  • Statistics and Probability

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