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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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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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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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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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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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Robust weighted ridge regression based on S – estimator
African Scientific Reports, 2023 Ordinary least squares (OLS) estimator performance is seriously threatened by correlated regressors often called multicollinearity. Multicollinearity is a situation when there is strong relationship between any two exogenous variables.
Taiwo Stephen Fayose +3 more
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Logistic regression diagnostics in ridge regression
Computational Statistics, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ozkale, M. Revan +2 more
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Maximum Likelihood Ridge Regression [PDF]
Stata Technical Bulletin, 1996 21 pages, 6 ...
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The efficiency of modified jackknife and ridge type regression estimators: a comparison [PDF]
Surveys in Mathematics and its Applications, 2008 A common problem in multiple regression models is multicollinearity, which produces undesirable effects on the least squares estimator. To circumvent this problem, two well known estimation procedures are often suggested in the literature.
Sharad Damodar Gore +2 more
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Metode Regresi Ridge Untuk Mengatasi Kasus Multikolinear
ComTech, 2011 Multicolinear is a case that occurs in multi-linear regression analysis. Using multicolinear, it will be difficult to separate the influence of each independent variable towards the response variables.
Margaretha Ohyver
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