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On Multivariate Ridge Regression [PDF]
Biometrika, 1987 A multivariate linear regression model with q responses as a linear function of p independent variables is considered with a \(p\times q\) parameter matrix B. The least-squares or normal-theory maximum likelihood estimate of B is deficient in that it takes no account of the `across regression' correlations, and ignores the Stein effect.
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Ridge Fuzzy Regression Modelling for Solving Multicollinearity
Mathematics, 2020 This paper proposes an α-level estimation algorithm for ridge fuzzy regression modeling, addressing the multicollinearity phenomenon in the fuzzy linear regression setting. By incorporating α-levels in the estimation procedure, we are able to construct a
Hyoshin Kim, Hye-Young Jung
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Ordinal Ridge Regression with Categorical Predictors [PDF]
, 2011 In multi-category response models categories are often ordered. In case of ordinal response models, the usual likelihood approach becomes unstable with ill-conditioned predictor space or when the number of parameters to be estimated is large relative to ...
Zahid, Faisal Maqbool
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Kernel Ridge Regression Inference
CoRR, 2023 We provide uniform confidence bands for kernel ridge regression (KRR), a widely used nonparametric regression estimator for nonstandard data such as preferences, sequences, and graphs. Despite the prevalence of these data--e.g., student preferences in school matching mechanisms--the inferential theory of KRR is not fully known.
Rahul Singh, Suhas Vijaykumar
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Employing Ridge Regression Technique in Prediction [PDF]
المجلة العراقية للعلوم الاحصائية, 2012 This paper is concerned with fitting some black box models. Some of them are, the outputs error model which contains the autoregressive and autoregressive moving average with additional inputs(ARX and ARMAX).The best model has been chosen which ...
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Two‐level preconditioning for Ridge Regression [PDF]
Numerical Linear Algebra with Applications, 2021 AbstractSolving linear systems is often the computational bottleneck in real‐life problems. Iterative solvers are the only option due to the complexity of direct algorithms or because the system matrix is not explicitly known. Here, we develop a two‐level preconditioner for regularized least squares linear systems involving a feature or data matrix ...
Joris Tavernier +3 more
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Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation [PDF]
Surveys in Mathematics and its Applications, 2009 Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR).
Sharad Damodar Gore, Feras Sh. M. Batah
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Abnormal Electricity Behavior Recognition of Graph Regularization Nonlinear Ridge Regression Model [PDF]
Jisuanji gongcheng, 2018 For the detection of abnormal electricity behavior by users,power companies usually adopt manual inspection methods,however,this method requires a lot of manpower and material resources,and is influened by subjective factors.Therefore,an abnormal ...
ZHANG Xiaofei,GENG Juncheng,SUN Yubao
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Minimax Ridge Regression Estimation. [PDF]
The Annals of Statistics, 1977 The technique of ridge regression, first proposed by Hoerl and Kennard, has become a popular tool for data analysts faced with a high degree of multicollinearity in their data. By using a ridge estimator, one hopes to both stabilize one's estimates (lower the condition number of the design matrix) and improve upon the squared error loss of the least ...
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To tune or not to tune, a case study of ridge logistic regression in small or sparse datasets
BMC Medical Research Methodology, 2021 Background For finite samples with binary outcomes penalized logistic regression such as ridge logistic regression has the potential of achieving smaller mean squared errors (MSE) of coefficients and predictions than maximum likelihood estimation.
Hana Šinkovec +3 more
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