On the Performance of Principal Component Liu-Type Estimator under the Mean Square Error Criterion
Journal of Applied Mathematics, 2013 Wu (2013) proposed an estimator, principal component Liu-type estimator, to overcome multicollinearity. This estimator is a general estimator which includes ordinary least squares estimator, principal component regression estimator, ridge estimator, Liu ...
Jibo Wu
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MENGATASI PENCILAN PADA PEMODELAN REGRESI LINEAR BERGANDA DENGAN METODE REGRESI ROBUST PENAKSIR LMS
Barekeng, 2019 Ordinary Least Squares (OLS) is frequent used method for estimating parameters. OLS estimator is not a robust regression procedure for the presence of outliers, so the estimate becomes inappropriate.
Farida Daniel
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A Comparison of Ordinary Least Squares and Least Absolute Error Estimation [PDF]
Econometric Theory, 1988 In a linear-regression model with heteroscedastic errors, we consider two tests: a Hausman test comparing the ordinary least squares (OLS) and least absolute error (LAE) estimators and a test based on the signs of the errors from OLS. It turns out that these are related by the well-known equivalence between Hausman and the generalized method of moments
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The performance of some new estimated ridge parameter regression model [PDF]
مجلة جامعة الانبار للعلوم الصرفةIn the presence of high correlation between the independent variables in the linear regression model, which is known as the multicollinearity problem, the ordinary least squares estimator produces large variations in the sample. To overcome this problem,
Fatima ALfahdawe, Mustafa Alheety
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A Generalized Linear Transformation and Its Effects on Logistic Regression
Mathematics, 2023 Linear transformations such as min–max normalization and z-score standardization are commonly used in logistic regression for the purpose of scaling.
Guoping Zeng, Sha Tao
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Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation [PDF]
Surveys in Mathematics and its Applications, 2009 Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR).
Sharad Damodar Gore, Feras Sh. M. Batah
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Mathematics, 2020 In the modeling and analysis of reliability data via the Lindley distribution, the maximum likelihood estimator is the most commonly used for parameter estimation. However, the maximum likelihood estimator is highly sensitive to the presence of outliers.
Muhammad Aslam Mohd Safari +2 more
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An Unbiased Two-Parameter Estimation with Prior Information in Linear Regression Model
The Scientific World Journal, 2014 We introduce an unbiased two-parameter estimator based on prior information and two-parameter estimator proposed by Özkale and Kaçıranlar, 2007. Then we discuss its properties and our results show that the new estimator is better than the two-parameter ...
Jibo Wu
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Two-Parameter Modified Ridge-Type M-Estimator for Linear Regression Model
The Scientific World Journal, 2020 The general linear regression model has been one of the most frequently used models over the years, with the ordinary least squares estimator (OLS) used to estimate its parameter.
Adewale F. Lukman +3 more
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Robust-stein estimator for overcoming outliers and multicollinearity
Scientific Reports, 2023 Linear regression models with correlated regressors can negatively impact the performance of ordinary least squares estimators. The Stein and ridge estimators have been proposed as alternative techniques to improve estimation accuracy.
Adewale F. Lukman +3 more
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