Maximum correntropy criterion regression models with tending-to-zero scale parameters
Journal of Statistical Planning and Inference, ISSN: 0378-3758, Vol: 231, Page: 106134
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
Maximum correntropy criterion regression (MCCR) models have been well studied within the theoretical framework of statistical learning when the scale parameters take fixed values or go to infinity. This paper studies MCCR models with tending-to-zero scale parameters. It is revealed that the optimal learning rate of MCCR models is O(n−1) in the asymptotic sense when the sample size n goes to infinity. In the case of finite samples, the performance and robustness of MCCR, Huber and the least square regression models are compared. The applications of these three methods to real data are also demonstrated.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0378375823001039; http://dx.doi.org/10.1016/j.jspi.2023.106134; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85180089433&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0378375823001039; https://dx.doi.org/10.1016/j.jspi.2023.106134
Elsevier BV
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