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Large-scale regression with non-convex loss and penalty

2020; Elsevier BV; Volume: 157; Linguagem: Inglês

10.1016/j.apnum.2020.07.006

1873-5460

Alessandro Buccini, Omar De la Cruz Cabrera, Marco Donatelli, Andrea Martinelli, Lothar Reichel,

Image and Signal Denoising Methods

We describe a computational method for parameter estimation in linear regression, that is capable of simultaneously producing sparse estimates and dealing with outliers and heavy-tailed error distributions. The method used is based on the image restoration method proposed in Huang et al. (2017) [13]. It can be applied to problems of arbitrary size. The choice of certain parameters is discussed. Results obtained for simulated and real data are presented.