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Volume 39, Issue 3
Robust Model Averaging Method Based on LOF Algorithm

Fan Wang, Kang You & Guohua Zou

DOI: 10.4208/cmr.2022-0046

Commun. Math. Res., 39 (2023), pp. 386-413.

Published online: 2023-04

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  • Abstract

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.

  • Keywords

Outliers, LOF algorithm, robust model averaging, asymptotic optimality, consistency.

  • AMS Subject Headings

C51, C53

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{CMR-39-386, author = {Wang , FanYou , 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 = {

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.

}, issn = {2707-8523}, doi = {https://doi.org/10.4208/cmr.2022-0046}, url = {http://global-sci.org/intro/article_detail/cmr/21608.html} }
TY - JOUR T1 - Robust Model Averaging Method Based on LOF Algorithm AU - Wang , Fan AU - You , Kang AU - Zou , Guohua JO - Communications in Mathematical Research VL - 3 SP - 386 EP - 413 PY - 2023 DA - 2023/04 SN - 39 DO - http://doi.org/10.4208/cmr.2022-0046 UR - https://global-sci.org/intro/article_detail/cmr/21608.html KW - Outliers, LOF algorithm, robust model averaging, asymptotic optimality, consistency. AB -

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.

Wang , FanYou , Kang and Zou , Guohua. (2023). Robust Model Averaging Method Based on LOF Algorithm. Communications in Mathematical Research . 39 (3). 386-413. doi:10.4208/cmr.2022-0046
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