Results 11 to 20 of about 7,731 (299)
A New Biased Estimator to Combat the Multicollinearity of the Gaussian Linear Regression Model
Stats, 2020 In a multiple linear regression model, the ordinary least squares estimator is inefficient when the multicollinearity problem exists. Many authors have proposed different estimators to overcome the multicollinearity problem for linear regression models ...
Issam Dawoud, B. M. Golam Kibria
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Lietuvos Matematikos Rinkinys, 2023 In this paper we present estimated generalized least squares (EGLS) estimator for the coefficient vector β in the linear regression model y = βX + ε, where disturbance term can be heteroskedastic.
Alfredas Račkauskas, Danas Zuokas
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Scientific African, 2023 Scholars usually adopt the method of least squared to model the relationship between a response variable and two or more explanatory variables. Ordinary least squares estimator's performance is good when there is no outliers and multicollinearity in the ...
K.C. Arum +5 more
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Shiller's Distributed Lag Estimator: Exact Finite Sample Results [PDF]
The Egyptian Statistical Journal, 1988 This paper is concerned with Shiner's estimator of papameters in a polynomial distributed lag models. The purpose here is to derive the exact moments of Shiller's estimator analytically, and compare its mean square error (MSE) with (1) MSE of ordinary ...
G.A. Ghazal, A.H. Harroun
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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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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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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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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 ...
Shalabh, Shalabh, Heumann, Christian
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