Dynamic system multivariate calibration by system identification methods [PDF]
Modeling, Identification and Control, 1998 In the first part of the paper, the optimal estimator for normally nonmeasured primary outputs from a linear and time invariant dynamic system is developed.
Rolf Ergon
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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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Superiority of the MCRR Estimator Over Some Estimators In A Linear Model [PDF]
المجلة العراقية للعلوم الاحصائية, 2011 Modified (r, k) class ridge regression (MCRR) which includes unbiased ridge regression (URR), (r, k) class, principal components regression (PCR) and the ordinary least squares (OLS) estimators is proposed in regression analysis, to overcome the problem ...
Feras Sh. M. Batah
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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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The Traditional Ordinary Least Squares Estimator under Collinearity [PDF]
Journal of Biometrics & Biostatistics, 2015 In a multiple regression analysis, it is usually difficult to interpret the estimator of the individual coefficients if the explanatory variables are highly inter-correlated. Such a problem is often referred to as the multicollinearity problem. There exist several ways to solve this problem. One such way is ridge regression.
Ghadban AK, Iguernane M
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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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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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Simultaneous Prediction of Actual and Average Values of Study Variable Using Stein-rule Estimators [PDF]
, 2011 The simultaneous prediction of average and actual values of study variable in a linear regression model is considered in this paper. Generally, either of the ordinary least squares estimator or Stein-rule estimators are employed for the construction of ...
Heumann, Christian, Shalabh, Shalabh
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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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