Results 61 to 70 of about 5,491 (290)
A New Type Iterative Ridge Estimator: Applications and Performance Evaluations
Journal of Mathematics, 2022 The usage of the ridge estimators is very common in presence of multicollinearity in multiple linear regression models. The ridge estimators are used as an alternative to ordinary least squares in case of multicollinearity as they have lower mean square ...
Aydın Karakoca
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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
wiley +1 more source
On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression Estimators
J. Mach. Learn. Res., 2020 We study the problem of estimating the derivatives of a regression function, which has a wide range of applications as a key nonparametric functional of unknown functions. Standard analysis may be tailored to specific derivative orders, and parameter tuning remains a daunting challenge particularly for high-order derivatives.
Zejian Liu, Meng Li
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Time‐Dependent Oxidation and Scale Evolution of a Wrought Co/Ni‐Based Superalloy
Advanced Engineering Materials, EarlyView.This study shows how a new wrought Co/Ni‐based superalloy resists oxidation at 800 ∘$^\circ$C. The oxide scale changes from rough, fast‐growing spinel to a dense, protective chromia–alumina layer. Atom probe analysis reveals tiny refractory‐rich bubbles at the interface that mark the transition to long‐term, diffusion‐controlled protection ...
Cameron Crabb +6 more
wiley +1 more source
Ridge-Type Estimators for Regression Analysis
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1974 Summary An examination of the mean-square error properties of a class of shrinkage estimators for the normal regression model leads to a new derivation of the Hoerl–Kennard (1970) Ridge estimator and its generalization. Comparison is made with the James–Stein estimator, and with the generalized-inverse estimator proposed by Marquardt ...
Goldstein, M., Smith, A. F. M.
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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
wiley +1 more source
Almost unbiased modified ridge-type estimator: An application to tourism sector data in Egypt
Heliyon, 2022 This paper introduces an almost unbiased modified ridge-type estimator (AUMRTE) to avoid problems arising from multicollinearity. This estimator has the important features of the two important shrinkage estimators, the modified ridge-type estimator (MRTE)
Tarek Mahmoud Omara
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Electrified Damage in Motion Systems
Advanced Engineering Materials, EarlyView.The electrified damage in motion systems is a fundamental framework presenting the degradation pathway arising from the coupling of electrical energy transport with mechanical contact and interfacial chemistry. The framework positions electrified damage as a distinct degradation regime with unique characteristic surface morphologies and failures of ...
M. Humaun Kabir +2 more
wiley +1 more source
Kuwait Journal of Science, 2023 Ridge regression is employed to estimate the regression parameters while circumventing the multicollinearity among independent variables. The ridge parameter plays a vital role as it controls bias-variance tradeoff. Several methods for choosing the ridge
Irum Sajjad Dar +3 more
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