Results 61 to 70 of about 3,667,438 (313)
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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Continuum Regression and Ridge Regression
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1993 SUMMARY We demonstrate the close relationship between first-factor continuum regression and standard ridge regression. The difference is that continuum regression inserts a scalar compensation factor for that part of the shrinkage in ridge regression that has no connection with tendencies towards collinearity.
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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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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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Econometric ridge regression models of risk-sensitive sunflower yield
Arquivo Brasileiro de Medicina Veterinária e Zootecnia, 2021 The article considers econometric ridge regression models of the risk-sensitive sunflower yield on the example of an export-oriented agricultural crop.
M.I. Slozhenkina +6 more
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Mathematical and Computer Modelling, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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Modified Robust Ridge M-Estimators in Two-Parameter Ridge Regression Model
Mathematical Problems in Engineering, 2021 The methods of two-parameter ridge and ordinary ridge regression are very sensitive to the presence of the joint problem of multicollinearity and outliers in the y-direction. To overcome this problem, modified robust ridge M-estimators are proposed.
Seyab Yasin +5 more
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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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Influence Diagnostics in Two-Parameter Ridge Regression
, 2021 : Identifying influential observations is an important part of the model building process in linear regression. There are numerous diagnostic measures based on different approaches in linear regression analysis.
Yasin Asar, Murat Erisoglu
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