Results 101 to 110 of about 3,187,155 (299)
An expansion of the generalized ridge estimator in a linear regression model
Journal of the Japan Statistical Society, Japanese Issue, 1988 Summary: In a linear regression model, it was shown by \textit{A. E. Hoerl} and \textit{R. W. Kennard} [Technometrics 12, 55-67 (1970; Zbl 0202.172)] that the generalized ridge estimator has ``potentially'' smaller Mean Squared Error (MSE) as an alternative to the Ordinary Least Squares (OLS) estimator. We apply this estimator in a problem predicting a
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Modified Ridge Parameters for Seemingly Unrelated Regression Model [PDF]
In this paper, we modify a number of new biased estimators of seemingly unrelated regression (SUR) parameters which are developed by Alkhamisi and Shukur (2008), AS, when the explanatory variables are affected by multicollinearity.
Shukur , Ghazi +2 more
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Optimization of Ridge Parameters in Multivariate Generalized Ridge Regression by Plug-in Methods [PDF]
, 2020 Generalized ridge (GR) regression for a univariate linear model was proposed simultaneously with ridge regression ...
Isamu Nagai +2 more
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Penalized Regression with Correlation Based Penalty [PDF]
, 2006 A new regularization method for regression models is proposed. The criterion to be minimized contains a penalty term which explicitly links strength of penalization to the correlation between predictors.
Gerhard Tutz +3 more
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Advanced Electronic Materials, EarlyView.A compact QASRR‐based THz metamaterial absorber enables polarization‐insensitive dual‐band absorption and skin‐cancer‐related refractive‐index sensing through measurable resonance shifts. Field, surface‐current, and circuit analyses clarify the dual‐resonance mechanism, while StackNet‐assisted prediction accurately estimates the simulated absorption ...
Md. Murad Kabir Nipun +5 more
wiley +1 more source
Generalized ridge estimator and model selection criteria in multivariate linear regression
Journal of Multivariate Analysis, 2018 We propose new model selection criteria based on generalized ridge estimators dominating the maximum likelihood estimator under the squared risk and the Kullback-Leibler risk in multivariate linear regression. Our model selection criteria have the following favorite properties: consistency, unbiasedness, uniformly minimum variance.
Yuichi Mori, Taiji Suzuki
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Difference based Ridge and Liu type Estimators in Semiparametric Regression Models [PDF]
We consider a difference based ridge regression estimator and a Liu type estimator of the regression parameters in the partial linear semiparametric regression model, y = Xβ + f + ε.
Wolfgang Karl Härdle +2 more
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Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
Advanced Energy Materials, EarlyView.In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen +4 more
wiley +1 more source
Generalized Ridge Regression: Applications to Nonorthogonal Linear Regression Models
This paper analyzes the possibilities of using the generalized ridge regression to mitigate multicollinearity in a multiple linear regression model. For this purpose, we obtain the expressions for the estimated variance, the coefficient of variation, the coefficient of correlation, the variance inflation factor and the condition number.
Gómez, Román Salmerón +2 more
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"Improved Empirical Bayes Ridge Regression Estimators under Multicollinearity" [PDF]
In this paper we consider the problem of estimating the regression parameters in a multiple linear regression model when the multicollinearity is present.Under the assumption of normality, we present three empirical Bayes estimators.
Tatsuya Kubokawa, M. S. Srivastava
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