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Ridge Regression: Some Simulations
Communications in Statistics - Simulation and Computation, 1975An algorithm is given for selacting the biasing paramatar, k, in RIDGE regrassion. By means of simulaction it is shown that the algorithm has the following properties: (i) it produces an aberaged squared error for the regrassion coafficiants that is les than least squares, (ii) the distribuction of squared arrots for the regression coafficiants has a ...
Hoerl, Arthur E. +2 more
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Reduced Rank Kernel Ridge Regression
Neural Processing Letters, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cawley, Gavin C., Talbot, Nicola L. C.
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Choquet Integral Ridge Regression
2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020The Choquet integral (ChI) is an aggregation function that is defined with respect to a fuzzy measure (FM). Many ChI-based decision aggregation methods have been proposed to learn the underlying FM. However, FM's boundary and monotonicity constraints have limited the applicability of such methods to decision-level fusion.
Siva K. Kakula +3 more
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Ridge Estimators in Logistic Regression
Applied Statistics, 1992Summary: In this paper it is shown how ridge estimators can be used in logistic regression to improve the parameter estimates and to diminish the error made by further predictions. Different ways to choose the unknown ridge parameter are discussed. The main attention focuses on ridge parameters obtained by cross-validation.
le Cessie, S., van Houwelingen, J. C.
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Objective Ridge Regression System
2020 7th International Conference on Information Science and Control Engineering (ICISCE), 2020In the actual system, it is very important to get enough training samples for the performance of the model. However, no matter how rich the collection is, there will be a problem: limited training sets cannot really explain the label of these. So we propose an Objective Ridge Regression System (ORRS) to solve this problem in the classification stage ...
Changmao Yang, Jie Xu, Sicong Gong
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Journal of Chemometrics, 1997
Ridge regression (RR) and principal component regression (PCR) are two popular methods intended to overcome the problem of multicollinearity which arises with spectral data. The present study compares the performances of RR and PCR in addition to ordinary least squares (OLS) and partial least squares (PLS) on the basis of two data sets.
Vigneau, Evelyne +3 more
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Ridge regression (RR) and principal component regression (PCR) are two popular methods intended to overcome the problem of multicollinearity which arises with spectral data. The present study compares the performances of RR and PCR in addition to ordinary least squares (OLS) and partial least squares (PLS) on the basis of two data sets.
Vigneau, Evelyne +3 more
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Detection of influential observations in ridge regression and modified ridge regression
Model Assisted Statistics and Applications, 2012The detection of influential observations is important because of their unduly large influence on the regression analysis results. Numerous diagnostics on identifying these observations are developed in the regression analysis. Pena's statistic is one of the proposed diagnostics. In this study, Pena's approach is formulated to ridge regression (RR) and
Türkan, Semra, Toktamış, Öniz
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International Journal of Fuzzy Systems, 2019
Ridge regression model is a widely used model with many successful applications, especially in managing correlated covariates in a multiple regression model. Multicollinearity represents a serious threat in fuzzy regression models as well. We address this issue by combining ridge regression with the fuzzy regression model.
Seung Hoe Choi +2 more
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Ridge regression model is a widely used model with many successful applications, especially in managing correlated covariates in a multiple regression model. Multicollinearity represents a serious threat in fuzzy regression models as well. We address this issue by combining ridge regression with the fuzzy regression model.
Seung Hoe Choi +2 more
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Technometrics, 1978
In this paper we examine the relationship between the general ridge estimator and the standardized ridge estimator.
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In this paper we examine the relationship between the general ridge estimator and the standardized ridge estimator.
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IEEE Transactions on Aerospace and Electronic Systems, 1991
A sequential algorithm which closely approximates ridge regression is introduced, and it is pointed out that the desired sequential ridge estimator can be obtained by properly choosing the free parameters of a startup technique for ordinary sequential least squares estimation.
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A sequential algorithm which closely approximates ridge regression is introduced, and it is pointed out that the desired sequential ridge estimator can be obtained by properly choosing the free parameters of a startup technique for ordinary sequential least squares estimation.
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