Bagging-Enhanced Sampling Schedule for Functional Quadratic Regression

Hyungmin Rha; Ming Hung Kao; Rong Pan

doi:10.1007/s42519-021-00223-x

Bagging-Enhanced Sampling Schedule for Functional Quadratic Regression

CLAS-NS: Mathematical and Statistical Sciences, School of (SMSS)

Research output: Contribution to journal › Article › peer-review

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	https://doi.org/10.1007/s42519-021-00223-x
State	Published - Dec 2021

Keywords

Bagging
Functional data analysis
Functional principal component
Functional regression model

ASJC Scopus subject areas

Statistics and Probability

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10.1007/s42519-021-00223-x

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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.",

keywords = "Bagging, Functional data analysis, Functional principal component, Functional regression model",

author = "Hyungmin Rha and Kao, {Ming Hung} and Rong Pan",

note = "Funding Information: This research was supported by National Science Foundation Grants CMMI-17-26445 and DMS-13-52213. The authors are grateful to anonymous reviewers for their comments and suggestions, which resulted in an improvement of the presentation of the paper. Publisher Copyright: {\textcopyright} 2021, Grace Scientific Publishing.",

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AU - Kao, Ming Hung

AU - Pan, Rong

N1 - Funding Information: This research was supported by National Science Foundation Grants CMMI-17-26445 and DMS-13-52213. The authors are grateful to anonymous reviewers for their comments and suggestions, which resulted in an improvement of the presentation of the paper. Publisher Copyright: © 2021, Grace Scientific Publishing.

PY - 2021/12

Y1 - 2021/12

N2 - 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.

AB - 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.

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KW - Functional principal component

KW - Functional regression model

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Bagging-Enhanced Sampling Schedule for Functional Quadratic Regression

Abstract

Keywords

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