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Approximated Uncertainty Propagation of Correlated Independent Variables Using the Ordinary Least Squares Estimator [PDF]
MoleculesFor chemical measurements, calibration is typically conducted by regression analysis. In many cases, generalized approaches are required to account for a complex-structured variance–covariance matrix of (in)dependent variables. However, in the particular
Jeong Sik Lim +2 more
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A New Convex Estimator Combining Ridge and Ordinary Least Squares Estimators [PDF]
مجلة جامعة الانبار للعلوم الصرفةIn the presence of high correlation between the independent variables in the linear regression model, which is known as the multicollinearity problem, the ordinary least squares estimator produce large variations in the sample.
Karam Al-janabi, Mustafa Alheety
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A New Mixed Biased Estimator for Ill‐Conditioning Challenges in Linear Regression Model With Chemometrics Applications [PDF]
Analytical Science AdvancesIn linear regression models, the ordinary least squares (OLS) method is used to estimate the unknown regression coefficients. However, the OLS estimator may provide unreliable estimates in non‐orthogonal models.
Muhammad Amin +3 more
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Temporal Aggregation and Ordinary Least Squares Estimation of Cointegrating Regressions [PDF]
, 2000 The paper derives the asymptotic distribution of the ordinary least squares estimator of cointegrating vectors with temporally aggregated time series.
Gabriel Pons Rotger
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A new biased regression estimator: Theory, simulation and application
Scientific African, 2022 The linear regression model explores the relationship between a response variable and one or more independent variables. The ordinary least squared estimator is usually adopted to estimate the parameters of the model when the independent variables are ...
Issam Dawoud +2 more
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ORDINARY LEAST SQUARES ESTIMATION OF A DYNAMIC GAME MODEL [PDF]
International Economic Review, 2016 Estimation of dynamic games is known to be a numerically challenging task. A common form of the payoff functions employed in practice takes the linear‐in‐parameter specification. We show a least squares estimator taking a familiar OLS/GLS expression is available in such a case. Our proposed estimator has a closed form.
Miessi Sanches, F.A. +2 more
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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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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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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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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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