@article{Lai2024, 
author = {Peng Lai and Wenxin Tian and Yanqiu Zhou},
title = {Semi-supervised estimation for the varying coefficient regression model},
year = {2024},
journal = {AIMS Mathematics},
volume = {9},
number = {1},
pages = {55-72},
keywords = {semi-supervised learning, varying coefficient regression model, intercept model, locally weighted regression model},
url = {https://www.sciopen.com/article/10.3934/math.2024004},
doi = {10.3934/math.2024004},
abstract = {In many cases, the 'labeled' outcome is difficult to observe and may require a complicated or expensive procedure, and the predictor information is easy to be obtained. We propose a semi-supervised estimator for the one-dimensional varying coefficient regression model which improves the conventional supervised estimator by using the unlabeled data efficiently. The semi-supervised estimator is proposed by introducing the intercept model and its asymptotic properties are proven. The Monte Carlo simulation studies and a real data example are conducted to examine the finite sample performance of the proposed procedure.}
}