On the Performance of the Jackknifed Modified Ridge Estimator in the Linear Regression Model with Correlated or Heteroscedastic Errors

Li, Yalian; Yang, Hu

doi:10.1080/03610926.2010.491589

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…The modified ordinary Jackknifed ridge regression estimator (MOJR) was proposed when the ridge parameter (k) is constant for the diameter elements of the information matrix (19) It was suggested that when applied differencing method to model (1), the estimator (…”

Section: Difference-based Modified Jackknifed Generalized Ridge Regression Estimator (Dmjgr)mentioning

confidence: 99%

Comparison of Some Suggested Estimators Based on Differencing Technique in the Partial Linear Model Using Simulation

Hussein¹

2019

Baghdad Sci.J

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In this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized jackknifed ridge regression estimator(DGJR), in estimating the parameters of linear part of the partially linear model. As for the nonlinear part represented by the nonparametric function, it was estimated using Nadaraya Watson smoother. The partially linear model was compared using these proposed methods with other estimators based on differencing technique through the MSE comparison criterion in simulation study.

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Section: Difference-based Modified Jackknifed Generalized Ridge Regression Estimator (Dmjgr)mentioning

confidence: 99%

Comparison of Some Suggested Estimators Based on Differencing Technique in the Partial Linear Model Using Simulation

Hussein¹

2019

Baghdad Sci.J

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“…But when these assumptions are violated, these methods do not yield the desirable results and leads to problems such as heteroscedasticity and autocorrelation [4]. Li, & Yang [5,7] suggested Jackknifed Modified Ridge Estimator (JMRE) and it was shown that it superior to other models.…”

Section: Introductionmentioning

confidence: 99%

Ridge Estimation's Effectiveness for Multiple Linear Regression with Multicollinearity: An Investigation Using Monte-Carlo Simulations

Obadina

Adedotun

Odusanya³

2021

J. Nig. Soc. Phys. Sci.

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The goal of this research is to compare multiple linear regression coefficient estimations with multicollinearity. In order to quantify the effectiveness of estimations by the mean of average mean square error, the ordinary least squares technique (OLS), modified ridge regression method (MRR), and generalized Liu-Kejian method (LKM) are compared (AMSE). For this study, the simulation scenarios are 3 and 5 independent variables with zero mean normally distributed random error of variance 1, 5, and 10, three correlation coefficient levels; i.e., low (0.2), medium (0.5), and high (0.8) are determined for independent variables, and all combinations are performed with sample sizes 15, 55, and 95 by Monte Carlo simulation technique for 1,000 times in total. As the sample size rose, the AMSE decreased. The MRR and LKM both outperformed the LSM. At random error of variance 10, the MRR is the most suitable for all circumstances.

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“…Hence, is the ridge parameter (see Li & Yang, 2011). The JMR estimator of α is given as Hoerl, Kennard, and Baldwin (1975) suggested the value of 'k' should be chosen small enough so the mean squared error of ridge estimator is less than the mean squared error of LS estimator.…”

Section: Ridge Regression Estimator (Rr)mentioning

confidence: 99%

“…Involving such problems as heteroscedasticity and autocorrelation few methods including Trenkler (1984), Firinguetti (1989), Bayhan and Bayhan (1998), Özkale (2008), Alheety and Kibria (2009) are available in the present literature. Recently, Li and Yang (2011) suggested Jackknifed Modified Ridge Estimator (JMRE) and show that it superior to the generalized least squares estimate, the generalized modified ridge estimator and the generalized jackknifed ridge estimator, to overcome multicollinearity in the presence of a linear regression model with correlated or heteroscedastic errors.…”

Section: Introductionmentioning

confidence: 99%

Improved Ridge Estimator in Linear Regression with Multicollinearity, Heteroscedastic Errors and Outliers

Dorugade¹

2016

J. Mod. App. Stat. Meth.

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This paper introduces a new estimator, of ridge parameter k for ridge regression and then evaluated by Monte Carlo simulation. We examine the performance of the proposed estimators compared with other well-known estimators for the model with heteroscedastics and/or correlated errors, outlier observations, non-normal errors and suffer from the problem of multicollinearity. It is shown that proposed estimators have a smaller MSE than the ordinary least squared estimator (LS), Hoerl and Kennard (1970) estimator (RR), jackknifed modified ridge (JMR) estimator, and Jackknifed Ridge M-estimator (JRM).

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On the Performance of the Jackknifed Modified Ridge Estimator in the Linear Regression Model with Correlated or Heteroscedastic Errors

Cited by 6 publications

References 15 publications

Comparison of Some Suggested Estimators Based on Differencing Technique in the Partial Linear Model Using Simulation

Comparison of Some Suggested Estimators Based on Differencing Technique in the Partial Linear Model Using Simulation

Ridge Estimation's Effectiveness for Multiple Linear Regression with Multicollinearity: An Investigation Using Monte-Carlo Simulations

Improved Ridge Estimator in Linear Regression with Multicollinearity, Heteroscedastic Errors and Outliers

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