Results 11 to 20 of about 636 (80)
A New Quantile-Based Approach for LASSO Estimation
Mathematics, 2023 Regularization regression techniques are widely used to overcome a model’s parameter estimation problem in the presence of multicollinearity. Several biased techniques are available in the literature, including ridge, Least Angle Shrinkage Selection ...
Ismail Shah +4 more
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Entropy, 2022 This study aims to propose modified semiparametric estimators based on six different penalty and shrinkage strategies for the estimation of a right-censored semiparametric regression model.
Syed Ejaz Ahmed +2 more
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Frontiers in Forests and Global Change, 2021 Forest information is requested at many levels and for many purposes. Sampling-based national forest inventories (NFIs) can provide reliable estimates on national and regional levels.
Magnus Ekström +2 more
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Survival prediction based on compound covariate under Cox proportional hazard models. [PDF]
PLoS ONE, 2012 Survival prediction from a large number of covariates is a current focus of statistical and medical research. In this paper, we study a methodology known as the compound covariate prediction performed under univariate Cox proportional hazard models.
Takeshi Emura +2 more
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Improved Penalty Strategies in Linear Regression Models
Revstat Statistical Journal, 2017 We suggest pretest and shrinkage ridge estimation strategies for linear regression models. We investigate the asymptotic properties of suggested estimators.
Bahadır Yüzbaşı +2 more
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Performance of LASSO and Elastic net estimators in Misspecified Linear Regression Model
Ceylon Journal of Science, 2019 Ridge Estimator (RE) has been used as an alternative estimator for Ordinary Least Squared Estimator (OLSE) to handle multicollinearity problem in the linear regression model. However, it introduces heavy bias when the number of predictors is high, and it
M. Kayanan, P. Wijekoon
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a simulation comparison of Ridge regression estimators with Lars
پژوهشهای ریاضی, 2022 Introduction Regression analysis is a common method for modeling relationships between variables. Usually Ordinary Least Squares method is applied to estimate regression model parameters.
Roshanak Alimohammadi, Jaleh Bahari
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Penalty Strategies in Semiparametric Regression Models
Mathematical and Computational ApplicationsThis study includes a comprehensive evaluation of six penalty estimation strategies for partially linear models (PLRMs), focusing on their performance in the presence of multicollinearity and their ability to handle both parametric and nonparametric ...
Ayuba Jack Alhassan +3 more
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Mathematical and Computational ApplicationsAccurate housing price prediction is important for market efficiency and purchasing decisions. However, multicollinearity among independent variables remains a major challenge in linear regression, causing variance inflation and reducing the reliability ...
Osman Ufuk Ekiz, Meltem Ekiz
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Journal of MathematicsThe accurate estimation of correlation matrices is a foundational challenge in high-dimensional statistics. The sample correlation matrix, while unbiased, suffers from high variance when the number of variables p is large relative to the sample size n ...
Muath Awadalla, Yücel Tandoğdu
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