@Article{CMR-39-3,
author = {Wang, Fan and You, Kang and Zou, Guohua},
title = {Robust Model Averaging Method Based on LOF Algorithm},
journal = {Communications in Mathematical Research },
year = {2023},
volume = {39},
number = {3},
pages = {386--413},
abstract = {<p style="text-align: justify;">Model averaging is a good alternative to model selection, which can
deal with the uncertainty from model selection process and make full use of
the information from various candidate models. However, most of the existing model averaging criteria do not consider the influence of outliers on the
estimation procedures. The purpose of this paper is to develop a robust model
averaging approach based on the local outlier factor (LOF) algorithm which
can downweight the outliers in the covariates. Asymptotic optimality of the
proposed robust model averaging estimator is derived under some regularity
conditions. Further, we prove the consistency of the LOF-based weight estimator tending to the theoretically optimal weight vector. Numerical studies
including Monte Carlo simulations and a real data example are provided to
illustrate our proposed methodology.</p>},
issn = {2707-8523},
doi = {https://doi.org/10.4208/cmr.2022-0046},
url = {https://global-sci.com/article/81465/robust-model-averaging-method-based-on-lof-algorithm}
}