Results 11 to 20 of about 884,004 (326)
Ordinary least squares estimation of parameters of linear model
Journal of Mathematics and Computer 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
semanticscholar +1 more source
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021 . This study entails generation of empirical ordinary least squares regression models to estimate water parameters. It uses remote sensing for environmental monitoring of Pasig River located in the Philippines.
J. E. Escoto +3 more
semanticscholar +1 more source
Geoscience Letters, 2022 The linear relationship between two stable water isotopes (δD and δ18O) has been used to examine the physical processes and movements or changes of three water phases (water vapor, liquid water and ice), including deuterium excess.
Jeonghoon Lee +3 more
doaj +1 more source
Quantum Regularized Least Squares [PDF]
Quantum, 2022 Linear regression is a widely used technique to fit linear models and finds widespread applications across different areas such as machine learning and statistics.
Shantanav Chakraborty +2 more
semanticscholar +1 more source
Adding bias to reduce variance in psychological results: A tutorial on penalized regression [PDF]
Tutorials in Quantitative Methods for Psychology, 2017 Regression models are commonly used in psychological research. In most studies, regression coefficients are estimated via maximum likelihood (ML) estimation.
Helwig, Nathaniel E.
doaj +1 more source
Journal of Separation Science, 2020 Ordinary least squares is widely applied as the standard regression method for analytical calibrations, and it is usually accepted that this regression method can be used for quantification starting at the limit of quantification.
J. M. Sánchez
semanticscholar +1 more source
RILS-ROLS: robust symbolic regression via iterated local search and ordinary least squares
Journal of Big Data, 2023 In this paper, we solve the well-known symbolic regression problem that has been intensively studied and has a wide range of applications. To solve it, we propose an efficient metaheuristic-based approach, called RILS-ROLS.
Aleksandar Kartelj, Marko Djukanović
doaj +1 more source
Mathematics, 2021 The current study examines the structural relationship between the academic performance exam scores of Korean middle school students and their after-school exercise hours.
Kyulee Shin, Sukkyung You, Mihye Kim
doaj +1 more source
Agricultural Economics (AGRICECON), 2020 Oil exporting economies were the most hit by the recent oil price shock that spills on the food market in an increasingly volatile macroeconomic environment. This paper examines and compares sub-samples [before crisis (2000 Q1-2013 Q1) and during crisis (
Ding Chen +3 more
doaj +1 more source
Stein-Rule Estimation under an Extended Balanced Loss Function [PDF]
, 2007 This paper extends the balanced loss function to a more general set up. The ordinary least squares and Stein-rule estimators are exposed to this general loss function with quadratic loss structure in a linear regression model.
---, Shalabh +2 more
core +1 more source