A New Mixed Biased Estimator for Ill‐Conditioning Challenges in Linear Regression Model With Chemometrics Applications [PDF]
Analytical Science AdvancesIn linear regression models, the ordinary least squares (OLS) method is used to estimate the unknown regression coefficients. However, the OLS estimator may provide unreliable estimates in non‐orthogonal models.
Muhammad Amin +3 more
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New Stochastic Restricted Biased Regression Estimators
MathematicsIn this paper, we propose three stochastic restricted biased estimators for the linear regression model. These new estimators generalize the least squares estimator, mixed estimator, and biased estimator. We derive the necessary and sufficient conditions
Hussein Eledum, Issam Dawoud
exaly +3 more sources
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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A Suggested Biased Estimator for Correcting Multicollinearity in Multinomial Logistic Regression [PDF]
The Egyptian Statistical Journal, 2014 Multinomial logistic model suffers from multicollinearity that causes wider confidence intervals and incorrect decisions for testing hypotheses for the regression parameters.
Rasha A.Farghali
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A new class of Poisson Ridge-type estimator
Scientific Reports, 2023 The Poisson Regression Model (PRM) is one of the benchmark models when analyzing the count data. The Maximum Likelihood Estimator (MLE) is used to estimate the model parameters in PRMs. However, the MLE may suffer from various drawbacks that arise due to
Esra Ertan, Kadri Ulaş Akay
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A New Two-Parameter Estimator for Beta Regression Model: Method, Simulation, and Application
Frontiers in Applied Mathematics and Statistics, 2022 The beta regression is a widely known statistical model when the response (or the dependent) variable has the form of fractions or percentages. In most of the situations in beta regression, the explanatory variables are related to each other which is ...
Mohamed R. Abonazel +3 more
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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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