Results 51 to 60 of about 3,667,438 (313)
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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IPS Intelligentsia Multidisciplinary Journal, 2023 Aims: This research work investigated the best regression technique in handling multicollinearity using the Ridge, Least Absolute Shrinkage and Selection Operator (LASSO) and Bridge regression models in comparison to Analysis and Prediction. Study Design:
Kelachi Enwere, E. Nduka, U. Ogoke
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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 ...
openaire +2 more sources
Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization [PDF]
Machine Learning: Science and Technology, 2020 Machine learning methods usually depend on internal parameters—so called hyperparameters—that need to be optimized for best performance. Such optimization poses a burden on machine learning practitioners, requiring expert knowledge, intuition or ...
A. Stuke, P. Rinke, M. Todorović
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Crosslingual Document Embedding as Reduced-Rank Ridge Regression [PDF]
, 2019 There has recently been much interest in extending vector-based word representations to multiple languages, such that words can be compared across languages.
Jaggi, Martin +4 more
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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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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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