Smoothly adaptively centered ridge estimator

نویسندگان

چکیده

With a focus on linear models with smooth functional covariates, we propose penalization framework (SACR) based the nonzero centered ridge, where center of penalty is adaptively reweighted, starting from ordinary ridge solution as initial center. In particular, introduce convex formulation that jointly estimates model’s coefficients and weight function, roughness center, constraints weights in order to recover possibly and/or sparse solution. This allows for non-iterative continuous variable selection mechanism, function can either inflate or deflate reducing unwanted shrinkage some coefficients. As empirical evidence interpretability predictive power our method, provide simulation study two real world spectroscopy applications, both classification regression.

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ژورنال

عنوان ژورنال: Journal of Multivariate Analysis

سال: 2022

ISSN: ['0047-259X', '1095-7243']

DOI: https://doi.org/10.1016/j.jmva.2021.104882