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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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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On the dominance of Mallows model averaging estimator over ordinary least squares estimator
Economics Letters, 2016 This note studies Mallows model averaging method for finite sample size situation. Sufficient conditions under which the model averaging estimator dominates the ordinary least squares estimator are provided with respect to mean squared error.
, Shangwei Zhao
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Improving the Ordinary Least Squares Estimator by Ridge Regression
OALib, 2022 In the presence of multicollinearity, ridge regression techniques result in estimated coefficients that are biased but have smaller variance than Ordinary Least Squares estimators and may, therefore, have a smaller Mean Squares Error (MSE).
G. Khalaf
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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.
G. Ak, M. Iguernane
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Ordinary least squares estimation of parameters of linear model
Journal of Mathematical and Computational Science, 2021 This research article primarily focuses on the method of ordinary least squares estimation of parameters of linear model. Here an innovative proof of Gauss-Markoff theorem for linear estimation has been presented.
K. Lakshmi +3 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.
Fabio Sanches +2 more
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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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Kernel-Based Regularized Least Squares in R (KRLS) and Stata (krls)
Journal of Statistical Software, 2017 The Stata package krls as well as the R package KRLS implement kernel-based regularized least squares (KRLS), a machine learning method described in Hainmueller and Hazlett (2014) that allows users to tackle regression and classification problems without
Jeremy Ferwerda +2 more
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Using Machine Learning to Improve Control for Confounding in the Dynamic Weighted Ordinary Least Squares Estimator of Optimal Adaptive Treatment Strategies [PDF]
Biometrical JournalDenis Talbot
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