Results 11 to 20 of about 177,424 (286)
Mendel, 2023 Can machine learning take a prediction to win an investment in ICO (Initial Coin Offering)? In this research work, our objective is to answer this question. Four popular and lower computational demanding approaches including Ridge regression (RR),
Toai Kim Tran +6 more
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Correlation Based Ridge Parameters in Ridge Regression with Heteroscedastic Errors and Outliers [PDF]
Journal of Statistical Theory and Applications (JSTA), 2015 This paper introduces some new estimators for estimating ridge parameter, based on correlation between response and regressor variables for ridge regression analysis.
A.V. Dorugade
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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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Low-Rank Tensor Thresholding Ridge Regression
IEEE Access, 2019 In the area of subspace clustering, methods combining self-representation and spectral clustering are predominant in recent years. For dealing with tensor data, most existing methods vectorize them into vectors and lose most of the spatial information ...
Kailing Guo +3 more
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Mathematics, 2022 For the linear model Y=Xb+error, where the number of regressors (p) exceeds the number of observations (n), the Elastic Net (EN) was proposed, in 2005, to estimate b.
Rajaram Gana
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Suggested Methods in Ridge Regression [PDF]
المجلة العراقية للعلوم الاحصائية, 2006 Three suggested procedures were adopted to determine the value of biasing parameter (k) in ridge regression: 1-fragmenting the ridge trace to groups each group contain semi-homogeneous absolute values of the estimated parameters, 2-rotating over the ...
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Adaptive ridge regression for rare variant detection. [PDF]
PLoS ONE, 2012 It is widely believed that both common and rare variants contribute to the risks of common diseases or complex traits and the cumulative effects of multiple rare variants can explain a significant proportion of trait variances.
Haimao Zhan, Shizhong Xu
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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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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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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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