New Shrinkage Parameters for the Liu-type Logistic Estimators

DOI10.1080/03610918.2014.995815zbMath1341.62233OpenAlexW2012780689MaRDI QIDQ3178513

Yasin Asar, Asir Genc

Publication date: 14 July 2016

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2014.995815

zbMATH Keywords

logistic regressionMLEmulticollinearityshrinkage parameter

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) General nonlinear regression (62J02)

Related Items (21)

Iterative algorithms of biased estimation methods in binary logistic regression ⋮ An improved and efficient biased estimation technique in logistic regression model ⋮ Performance of the almost unbiased ridge-type principal component estimator in logistic regression model ⋮ Shrinkage parameter selection via modified cross-validation approach for ridge regression model ⋮ Liu-type estimator for the gamma regression model ⋮ A new Liu-type estimator in binary logistic regression models ⋮ Logistic Liu estimator under stochastic linear restrictions ⋮ A new Liu-type estimator for the Inverse Gaussian Regression Model ⋮ Inverse Gaussian Liu-type estimator ⋮ A new estimator to control collinearity problems in correlated binary response ⋮ Optimal generalized logistic estimator ⋮ Restricted ridge estimator in the logistic regression model ⋮ On the restricted almost unbiased Liu estimator in the logistic regression model ⋮ Two parameter Ridge estimator in the inverse Gaussian regression model ⋮ Liu-type multinomial logistic estimator ⋮ Optimal stochastic restricted logistic estimator ⋮ A two-stage sparse logistic regression for optimal gene selection in high-dimensional microarray data classification ⋮ A new kind of stochastic restricted biased estimator for logistic regression model ⋮ On the stochastic restricted Liu-type maximum likelihood estimator in logistic regression model ⋮ Performance of ridge estimator in inverse Gaussian regression model ⋮ On a new class of binomial ridge-type regression estimators

Cites Work

• On Ridge Parameters in Logistic Regression
• Some Modifications for Choosing Ridge Parameters
• A Monte Carlo Study of Recent Ridge Parameters
• On Some Ridge Regression Estimators: An Empirical Comparisons
• Simultaneous prediction intervals for all distances from the “best”
• Choosing Ridge Parameter for Regression Problems
• Performance of Some New Ridge Regression Estimators
• Using Liu-Type Estimator to Combat Collinearity
• Liu-Type Logistic Estimator

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