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Efficiency of conformalized ridge regression [PDF]

CoRR, 2014
Conformal prediction is a method of producing prediction sets that can be applied on top of a wide range of prediction algorithms. The method has a guaranteed coverage probability under the standard IID assumption regardless of whether the assumptions (often considerably more restrictive) of the underlying algorithm are satisfied.
Evgeny Burnaev, Vladimir Vovk
openaire   +4 more sources

Ridge regression revisited [PDF]

Statistica Neerlandica, 2005
In general ridge (GR) regression p ridge parameters have to be determined, whereas simple ridge regression requires the determination of only one parameter. In a recent textbook on linear regression, Jürgen Gross argues that this constitutes a major complication.
de Boer, P.M.C., Hafner, C.M.
openaire   +5 more sources

Semi-Supervised Ridge Regression with Adaptive Graph-Based Label Propagation

Applied Sciences, 2018
In order to overcome the drawbacks of the ridge regression and label propagation algorithms, we propose a new semi-supervised classification method named semi-supervised ridge regression with adaptive graph-based label propagation (SSRR-AGLP).
Yugen Yi, Yuqi Chen, Jiangyan Dai, Xiaolin Gui, Chunlei Chen, Gang Lei, Wenle Wang   +6 more
doaj   +1 more source

ELM Ridge Regression Boosting

CoRR, 2023
We discuss a boosting approach for the Ridge Regression (RR) method, with applications to the Extreme Learning Machine (ELM), and we show that the proposed method significantly improves the classification performance and robustness of ELMs.
openaire   +2 more sources

Weighted Ridge Regression: Combining Ridge and Robust Regression Methods [PDF]

, 1973
This paper gives the formulas for and derivation of ridge regression methods when there are weights associated with each observation. A Bayesian motivation is used and various choices of k are discussed. A suggestion is made as to how to combine ridge regression with robust regression methods.
openaire   +2 more sources

Comparative Study in Controlling Outliers and Multicollinearity Using Robust Performance Jackknife Ridge Regression Estimator Based on Generalized-M and Least Trimmed Square Estimator

Jambura Journal of Mathematics
Regression analysis is one of the statistical methods used to determine the causal relationship between one or more explanatory variables to the affected variable.
Gustina Saputri, Netti Herawati, Tiryono Ruby, Khoirin Nisa   +3 more
doaj   +1 more source

Multilocus association mapping using generalized ridge logistic regression

BMC Bioinformatics, 2011
Background In genome-wide association studies, it is widely accepted that multilocus methods are more powerful than testing single-nucleotide polymorphisms (SNPs) one at a time.
Ott Jurg, Shen Yuanyuan, Liu Zhe
doaj   +1 more source

MODIFIED FUZZY-ROBUST RIDGE REGRESSION FOR MULTICOLLINEAR, OUTLIER-CONTAMINATED DATA

Science Journal of University of Zakho
Multicollinearity is known to have a significant impact on the stability of linear regression parameter estimation, while the presence of outliers tends to compound this problem. Ridge regression helps to improve the multicollinearity problem, but it is
Vaman M Salih, Shelan S Ismaeel
doaj   +1 more source

Maximum Likelihood Ridge Regression [PDF]

Stata Technical Bulletin, 1996
21 pages, 6 ...
openaire   +2 more sources

Sensor-Based Continuous Authentication Using Cost-Effective Kernel Ridge Regression

IEEE Access, 2018
People prefer to store important, private, and sensitive information on smartphones for convenient storage and fast access, such as photos and emails.
Yantao Li, Hailong Hu, Gang Zhou, Shaojiang Deng   +3 more
doaj   +1 more source