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2025-10-21
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Ridge Regression Under Dense Factor Augmented Models
This article establishes a comprehensive theory of the optimality, robustness, and cross-validation selection consistency for the ridge regression under factor-augmented models with possibly dense idi
DataCite Commons2024-02-13 更新280
Can’t ridge regression perform variable selection?
Ridge regression was introduced to deal with the instability issue of the ordinary least squares estimate due to multicollinearity. It essentially penalizes the least squares loss by applying a ridge
DataCite Commons2021-05-04 更新90
A note on ridge regression modeling techniques
In this study, the techniques of ridge regression model as alternative to the classical ordinary least square (OLS) method in the presence of correlated predictors were investigated. One of the basic
DataCite Commons2020-08-19 更新20
Distributed Generalized Cross-Validation for Divide-and-Conquer Kernel Ridge Regression and its Asymptotic Optimality
Tuning parameter selection is of critical importance for kernel ridge regression. To this date, data driven tuning method for divide-and-conquer kernel ridge regression (d-KRR) has been lacking in the
DataCite Commons2021-04-29 更新90
Fitting curve parameters.
This paper delves into the engineering applications of lime-stabilized red clay, a highly water-sensitive material, particularly in the context of the climatic conditions prevalent in the Dalian regio
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