Application of the ADMM Algorithm for a High-Dimensional Partially Linear Model

This paper focuses on a high-dimensional semi-parametric regression model in which a partially linear model is used for the parametric part and the B-spline basis function approach is used to estimate the unknown function for the non-parametric part. Within the framework of this model, the constrain...

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Main Authors: Aifen Feng, Xiaogai Chang, Youlin Shang, Jingya Fan
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/10/24/4767
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author Aifen Feng
Xiaogai Chang
Youlin Shang
Jingya Fan
author_facet Aifen Feng
Xiaogai Chang
Youlin Shang
Jingya Fan
author_sort Aifen Feng
collection DOAJ
description This paper focuses on a high-dimensional semi-parametric regression model in which a partially linear model is used for the parametric part and the B-spline basis function approach is used to estimate the unknown function for the non-parametric part. Within the framework of this model, the constrained least squares estimation is investigated, and the alternating-direction multiplier method (ADMM) is used to solve the model. The convergence is proved under certain conditions. Finally, numerical simulations are performed and applied to workers’ wage data from CPS85. The results show that the ADMM algorithm is very effective in solving high-dimensional partially linear models.
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spelling doaj.art-388b7c2095ca41cd91e77ca6b5d3d05b2023-11-24T16:29:22ZengMDPI AGMathematics2227-73902022-12-011024476710.3390/math10244767Application of the ADMM Algorithm for a High-Dimensional Partially Linear ModelAifen Feng0Xiaogai Chang1Youlin Shang2Jingya Fan3School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, ChinaSchool of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, ChinaSchool of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, ChinaSchool of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, ChinaThis paper focuses on a high-dimensional semi-parametric regression model in which a partially linear model is used for the parametric part and the B-spline basis function approach is used to estimate the unknown function for the non-parametric part. Within the framework of this model, the constrained least squares estimation is investigated, and the alternating-direction multiplier method (ADMM) is used to solve the model. The convergence is proved under certain conditions. Finally, numerical simulations are performed and applied to workers’ wage data from CPS85. The results show that the ADMM algorithm is very effective in solving high-dimensional partially linear models.https://www.mdpi.com/2227-7390/10/24/4767partially linear modelB-spline interpolationADMMvariational inequality
spellingShingle Aifen Feng
Xiaogai Chang
Youlin Shang
Jingya Fan
Application of the ADMM Algorithm for a High-Dimensional Partially Linear Model
Mathematics
partially linear model
B-spline interpolation
ADMM
variational inequality
title Application of the ADMM Algorithm for a High-Dimensional Partially Linear Model
title_full Application of the ADMM Algorithm for a High-Dimensional Partially Linear Model
title_fullStr Application of the ADMM Algorithm for a High-Dimensional Partially Linear Model
title_full_unstemmed Application of the ADMM Algorithm for a High-Dimensional Partially Linear Model
title_short Application of the ADMM Algorithm for a High-Dimensional Partially Linear Model
title_sort application of the admm algorithm for a high dimensional partially linear model
topic partially linear model
B-spline interpolation
ADMM
variational inequality
url https://www.mdpi.com/2227-7390/10/24/4767
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AT xiaogaichang applicationoftheadmmalgorithmforahighdimensionalpartiallylinearmodel
AT youlinshang applicationoftheadmmalgorithmforahighdimensionalpartiallylinearmodel
AT jingyafan applicationoftheadmmalgorithmforahighdimensionalpartiallylinearmodel