Choosing Ridge Parameter for Regression Problems

By G. Khalaf, Ghazi Shukur. 2005

261 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e6194ddc4d80b855921b60673921f35a89713522

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Citations in the corpus

A Poisson ridge regression estimator (2011, methodology): “The purpose of this study is to adopt and modify the new approaches mentioned in Kibria (2003), Khalaf and Shukur (2005), Alkhamisi et al. (2006), Alkhamisi and Shukur (2008) and very recently Muniz and Kibria (2009) to be applicable in Poisson regressions for count data, i.e. Poisson ridge…”
Improved Liu Estimators for the Poisson Regression Model (2012, methodology): “This method is a generalization of the Liu (1993) estimator originally developed for the linear regression model and will be generalized here to be used instead of the classical maximum likelihood (ML) method in the presence of multicollinearity since the mean squared error (MSE) of ML becomes infla”
Some Modifications for Choosing Ridge Parameters (2006, methodology): “The proposed new estimators are extended versions of that suggested by Khalaf and Shukur (2005). The properties of these estimators are compared with those of Hoerl and Kennard (1970a) and the OLS using the MSE criterion.”
New ridge parameters for ridge regression (2014, background): “…Nomura (1988), Heath and co-workers (1979), Nordberg (1982), Saleh and Kibria (1993), Haq and Kibria (1996), Kibria (2003), Pasha and Shah (2004), Khalaf and Shukur (2005), Norliza et al. (2006), Alkhamisi and Shukur (2007), Mardikyan and Cetin (2008), Dorugade and Kashid (2010) and Al-Hassan…”
Performance of a new ridge regression estimator (2010, methodology): “Although many estimators can be considered in this simulation study (Kibria, 2003; Khalaf and Shukur, 2005; Alkhamisi et al., 2006; Alkhamisi and Shukur, 2007 and Al-Hassan, 2008), we will only consider OLS, HK and HSL estimators and compare them with NHSL.”
Review and classications of the ridge parameter estimation techniques (2017, methodology): “Hacettepe Journal of Mathematics and Statistics Volume 46 (5) (2017), 953 967”
Alternative Method for Choosing Ridge Parameter for Regression (2010, methodology): “(8) (Masuo Nomura, [6]) (4) max 2 max 2 2 max ˆ ˆ ) 1 (( ) ˆ ( α λ σ σ λ + − − = p n kKS (Khalaf and Shukur, [3]) (9) All the methods of estimating ridge parameter are used in section 4.”
On Liu Estimators for the Logit Regression Model (2012, methodology): “The idea behind these proposed estimators are obtained from the work of Hoerl and Kennard (1970), Kibria (2003) and Khalaf and Shukur (2005) where several different methods of estimating the shrinkage parameter for linear ridge regression have been proposed.”
On Ridge Parameters in Logistic Regression (2011, background): “To mention a few, McDonald and Galarneau (1975), Lawless and Wang (1976), Saleh and Kibria (1993), Haq and Kibria (1996), Kibria (2003, 2004), Kibria and Saleh (2004), Khalaf and Shukur (2005), Alkhamisi et al. (2006), and Muniz and Kibria (2009).”
On Developing Ridge Regression Parameters: A Graphical investigation (2012, methodology): “These functions of the optimal value used firstly different quantiles such as the median and max just as in Khalaf and Shukur (2005) and secondly the square root transformation that was shown to work well in Alkhamisi and Shukur (2008). These ridge parameters correspond to:”