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Mathematical and Computer Modelling, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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The coefficient of determination in the ridge regression [PDF]
Communications in Statistics - Simulation and Computation, 2019 In a linear regression, the coefficient of determination, R2, is a relevant measure that represents the percentage of variation in the dependent variable that is explained by a set of independent variables. Thus, it measures the predictive ability of the estimated model. For an ordinary least squares (OLS) estimator, this coefficient is calculated from
Ainara Rodríguez-Sánchez +2 more
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Conformalized Kernel Ridge Regression [PDF]
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 2016 8 pages, 8 figures, 4 ...
Evgeny Burnaev, Ivan Nazarov
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Modified Ridge Estimator for Poisson Regression
Cumhuriyet Science JournalPoisson regression is a statistical model used to model the relationship between a count-valued-dependent variable and one or more independent variables. A frequently encountered problem when modeling such relationships is multicollinearity, which occurs
Shuaib Mursal Ibrahim, Aydın Karakoca
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KINERJA JACKKNIFE RIDGE REGRESSION DALAM MENGATASI MULTIKOLINEARITAS
E-Jurnal Matematika, 2014 Ordinary least square is a parameter estimations for minimizing residual sum of squares. If the multicollinearity was found in the data, unbias estimator with minimum variance could not be reached.
HANY DEVITA +2 more
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FEBS Open Bio, EarlyView.Activation of the mitochondrial protein OXR1 increases pSyn129 αSynuclein aggregation by lowering ATP levels and altering mitochondrial membrane potential, particularly in response to MSA‐derived fibrils. In contrast, ablation of the ER protein EMC4 enhances autophagic flux and lysosomal clearance, broadly reducing α‐synuclein aggregates.
Sandesh Neupane +11 more
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Ridge Regression and PLS Regression
American Review of Mathematics and Statistics, 2023 A brief review of Ridge Regression (RR) and PLS Regression (PLS) is presented. Process and Spectral data are used in the analysis. Both are low-rank data, which is common in chemometric work. The Ridge constant k is determined by minimizing the size of the residuals in Leave-one-out RR.
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Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
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Scalable Algorithms for the Sparse Ridge Regression [PDF]
SIAM Journal on Optimization, 2020 31 ...
Weijun Xie 0001, Xinwei Deng
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Advanced Engineering Materials, EarlyView.This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer +4 more
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