A New Biased Estimator to Combat the Multicollinearity of the Gaussian Linear Regression Model
Stats, 2020 In a multiple linear regression model, the ordinary least squares estimator is inefficient when the multicollinearity problem exists. Many authors have proposed different estimators to overcome the multicollinearity problem for linear regression models ...
I. Dawoud, B. M. G. Kibria
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A new biased regression estimator: Theory, simulation and application
Scientific African, 2022 The linear regression model explores the relationship between a response variable and one or more independent variables. The ordinary least squared estimator is usually adopted to estimate the parameters of the model when the independent variables are ...
Issam Dawoud +2 more
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Optimal Generalized Biased Estimator in Linear Regression Model
Open Journal of Statistics, 2015 The paper introduces a new biased estimator namely Generalized Optimal Estimator (GOE) in a multiple linear regression when there exists multicollinearity among predictor variables.
S. Arumairajan, P. Wijekoon
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On Restricted Shrinkage Jackknife Biased Estimator for Restricted Linear Regression Model [PDF]
مجلة جامعة الانبار للعلوم الصرفة, 2023 In restricted linear regression model, more methods proposed to address the Multicollinearity problem and the high variance. For example, shrinkage biased estimation and optimization (Lagrange function).
Ahmed Mohammed, Feras Algareri
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Evaluating AUC estimators across complex sampling designs: insights from COVID-19 patient data [PDF]
BMC Medical Research MethodologyPurpose Many studies in medical research are currently based on large-scale health surveys. Data collected in these surveys are usually obtained by following complex sampling designs, which include techniques such as stratification and clustering.
Amaia Iparragirre +2 more
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Buckley–James‐Type Estimator with Right‐Censored and Length‐Biased Data
Biometrics, 2011 J. Ning, J. Qin, Yu Shen
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Journal of New Theory, 2022 The sensitivity of the least-squares estimation in a regression model is impacted by multicollinearity and autocorrelation problems. To deal with the multicollinearity, Ridge, Liu, and Ridge-type biased estimators have been presented in the statistical ...
Tuğba Söküt Açar
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Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors [PDF]
Journal of Sciences, Islamic Republic of Iran, 2019 In the linear regression models with AR (1) error structure when collinearity exists, stochastic linear restrictions or modifications of biased estimators (including Liu estimators) can be used to reduce the estimated variance of the regression ...
Hoda Mohammadi, Abdolrahman Rasekh
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A Simulation Study of Some Restricted Estimators in Restricted Linear Regression Model
Tikrit Journal of Pure Science, 2021 When the multicollinearity exists in linear regression model, the result of the Restricted Least Square estimator (RLS) is unstable. So that, more researchers proposed the restricted biased estimators to improve the efficiency of RLS estimator.
Bader Aboud Mohammad +1 more
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Biased proportional hazard regression estimator in the existence of collinearity
Heliyon, 2023 This paper proposed a new biased proportional hazard regression (PHR) estimator which is the combination of elastic net proportional hazard regression (ENPHR) and principal components proportional hazard regression (PCPHR) estimator.
Anu Sirohi +3 more
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