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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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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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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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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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Ridge Regression and PLS Regression
American Review of Mathematics and Statistics, 2023 A brief review of Ridge Regression (RR) and PLS Regression (PLS) is presented. Process and Spectral data are used in the analysis. Both are low-rank data, which is common in chemometric work. The Ridge constant k is determined by minimizing the size of the residuals in Leave-one-out RR.
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Responsibility division method of harmonic sources in coal mine power system
Gong-kuang zidonghua, 2018 In view of the problem of collinearity of harmonic emission level evaluating method of coal mine power system based on multivariate linear regression, which leaded to the problem that evaluation result was greatly affected by abnormal value problem ...
GAO Yun, SU Jingwei
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Study of Some Kinds of Ridge Regression Estimators in Linear Regression Model
Tikrit Journal of Pure Science, 2020 In linear regression model, the biased estimation is one of the most commonly used methods to reduce the effect of the multicollinearity. In this paper, a simulation study is performed to compare the relative efficiency of some kinds of biased ...
Mustafa Nadhim Lattef, Mustafa I ALheety
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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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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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