Correlation Based Ridge Parameters in Ridge Regression with Heteroscedastic Errors and Outliers [PDF]
Journal of Statistical Theory and Applications (JSTA), 2015 This paper introduces some new estimators for estimating ridge parameter, based on correlation between response and regressor variables for ridge regression analysis.
A.V. Dorugade
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M Robust Weighted Ridge Estimator in Linear Regression Model
African Scientific Reports, 2023 Correlated regressors are a major threat to the performance of the conventional ordinary least squares (OLS) estimator. The ridge estimator provides more stable estimates in this circumstance.
Taiwo Stephen Fayose +2 more
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Moderate-Dimensional Inferences on Quadratic Functionals in Ordinary Least Squares [PDF]
Journal of the American Statistical Association, 2018 Statistical inferences for quadratic functionals of linear regression parameter have found wide applications including signal detection, global testing, inferences of error variance and fraction of variance explained.
Xiao Guo, Guang Cheng
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Comparison of Some Estimators under the Pitman’s Closeness Criterion in Linear Regression Model
Journal of Applied Mathematics, 2014 Batah et al. (2009) combined the unbiased ridge estimator and principal components regression estimator and introduced the modified r-k class estimator.
Jibo Wu
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A Modified New Two-Parameter Estimator in a Linear Regression Model
Modelling and Simulation in Engineering, 2019 The literature has shown that ordinary least squares estimator (OLSE) is not best when the explanatory variables are related, that is, when multicollinearity is present. This estimator becomes unstable and gives a misleading conclusion.
Adewale F. Lukman +3 more
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The VIF and MSE in Raise Regression
Mathematics, 2020 The raise regression has been proposed as an alternative to ordinary least squares estimation when a model presents collinearity. In order to analyze whether the problem has been mitigated, it is necessary to develop measures to detect collinearity after
Román Salmerón Gómez +3 more
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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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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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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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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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