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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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Effect of Multicollinearity on Power Rates of the Ordinary Least Squares Estimators [PDF]
Journal of Mathematics and Statistics, 2008 Summary: Inferences on the parameter estimates of the Ordinary Least Square (OLS) estimator in regression models when regressors exhibit multicollinearity is a problem in that large standard errors of the regression coefficients which cause low t-statistic values often result into the acceptance of the null hypothesis.
Alabi, O. O. +2 more
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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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Convex Combination of Ordinary Least Squares and Two-stage Least Squares Estimators
, 2015 In the presence of confounders, the ordinary least squares (OLS) estimator is known to be biased. This problem can be remedied by using the two-stage least squares (TSLS) estimator, based on the availability of valid instrumental variables (IVs). This reduction in bias, however, is offset by an increase in variance.
Ginestet, Cedric E. +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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Ridge Regression and Ill-Conditioning [PDF]
, 2014 Hoerl and Kennard (1970) suggested the ridge regression estimator as an alternative to the Ordinary Least Squares (OLS) estimator in the presence of multicollinearity.
Iguernane, Mohamed, Khalaf, Ghadban
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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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