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8 results for “Multicollinearity problem”
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Research ArticleOpen accessIn the keywordsEstimation Methods for Multicollinearity Proplem Combined with High Leverage Data PointsMoawad El-Fallah, Abd El-SallamProblem statement: Least Squares (LS) method has been the most popular method for estimating the parameters of a model due to its optimal properties and ease of computation. LS estimated regression may be seriously affected by multicollinearity which is a near linear dependency between two or more explanatory variables in the regression models. Although LS estimates are unbiased in the … …Journal of Mathematics and Statistics, Vol. 7, Issue 2 · 18 May 2011 · 7,617 accessesView PDFCite
Research ArticleOpen accessIn the abstractTwo-Stage Estimation in Inverse Problems using Combined Wavelet Thresholding and Penalized Maximum LikelihoodRobert G Aykroyd, Hassan AljohaniInverse problems occur in a wide range of practical scientific investigations where the variables of interest are only observed indirectly, such as magnetic and seismic imaging in geophysics, electrical tomography in industrial process monitoring, or PET scanning in medicine. Linear inverse problems can be thought of as highly multivariate regression problems with strong multicollinearity where the aim is to interpret … …Journal of Mathematics and Statistics, Vol. 13, Issue 3 · 29 Sep 2017 · 7,925 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Modification of the Ridge Type Regression EstimatorsMoawad El-Fallah Abd El-SalamProblem statement: Many regression estimators have been used to remedy multicollinearity problem. The ridge estimator has been the most popular one. However, the obtained estimate is biased. Approach: In this stuyd, we introduce an alternative shrinkage estimator, called modified unbiased ridge (MUR) estimator for coping with multicollinearity problem. This estimator is obtained from Unbiased Ridge Regression (URR) in the same … …American Journal of Applied Sciences, Vol. 8, Issue 1 · 31 Jan 2011 · 7,638 accessesView PDFCite
Research ArticleOpen accessIn the abstractEstimation under Multicollinearity: A Comparative Approach Using Monte Carlo MethodsD. A. Agunbiade, J. O. IyaniwuraProblem statement: A comparative investigation was done experimentally for 6 different Estimation Techniques of a just-identified simultaneous three-equation econometric model with three multi-collinear exogenous variables. Approach: The aim is to explore in depth the effects of the problems of multicollinearity and examine the sensitivity of findings to increasing sample sizes and increasing number of replications using the mean and total … …Journal of Mathematics and Statistics, Vol. 6, Issue 2 · 30 Jun 2010 · 7,531 accessesView PDFCite
Research ArticleOpen accessIn the abstractRobust Estimations as a Remedy for Multicollinearity Caused by Multiple High Leverage PointsArezoo Bagheri, Habshah MidiProblem statement: The Least Squares (LS) method has been the most popular technique for estimating the parameters of a model due to its optimal properties and ease of computation. LS estimated regression may be seriously disturbed by multicollinearity which is a near linear dependency between two or more explanatory variables in the regression models. Even though LS estimates are unbiased … …Journal of Mathematics and Statistics, Vol. 5, Issue 4 · 31 Dec 2009 · 8,247 accessesView PDFCite
Research ArticleOpen accessIn the abstractTwo-Step Robust Diagnostic Method for Identification of Multiple High Leverage PointsArezoo Bagheri, Habshah Midi, A.H.M. Rahmatullah ImonProblem statement: High leverage points are extreme outliers in the X-direction. In regression analysis, the detection of these leverage points becomes important due to their arbitrary large effects on the estimations as well as multicollinearity problems. Mahalanobis Distance (MD) has been used as a diagnostic tool for identification of outliers in multivariate analysis where it finds the distance between normal … …Journal of Mathematics and Statistics, Vol. 5, Issue 2 · 30 Jun 2009 · 8,306 accessesView PDFCite
Research ArticleOpen accessIn the abstractThe Performance of Latent Root-M based RegressionHabshah Midi, Lau Ung HuaProblem statement: In the presence of multicollinearity, the estimation of parameters in multiple linear regression models by means of Ordinary Least Squares (OLS) is known to suffer severe distortion. An alternative approach was to use the modified OLS which was based on the latent roots and latent vectors of the correlation matrix of the independent and dependent variables. This procedure … …Journal of Mathematics and Statistics, Vol. 5, Issue 1 · 31 Mar 2009 · 8,654 accessesView PDFCite
Research ArticleOpen accessIn the abstractEffect of Multicollinearity on Power Rates of the Ordinary Least Squares EstimatorsO.O. Alabi, Kayode Ayinde, T.O. OlatayoInferences on the parameter estimates of Ordinary Least Square (OLS) estimator in regression model when regressors exhibit multicollinearity is a problem in that large standard errors of the regression coefficients which cause low t-statistic value often result into the acceptance of the null hypothesis. This paper, therefore, makes efforts to investigate the effect of multicollinearity on the power rates of … …Journal of Mathematics and Statistics, Vol. 4, Issue 2 · 30 Jun 2008 · 7,880 accessesView PDFCite