Results 11 to 20 of about 132,600 (302)
Interference of sample size on multicollinearity diagnosis in path analysis [PDF]
Pesquisa Agropecuária Brasileira, 2018 : The objective of this work was to evaluate the interference of sample size on multicollinearity diagnosis in path analysis. From the analyses of productive traits of cherry tomato, two Pearson correlation matrices were obtained, one with severe ...
Bruno Giacomini Sari +5 more
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Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review
Mathematics, 2022 Technologies have driven big data collection across many fields, such as genomics and business intelligence. This results in a significant increase in variables and data points (observations) collected and stored.
Chan Jireh Yi-Le +2 more
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Decreasing Multicollinearity [PDF]
Sociological Methods & Research, 1979 When the multicollinearity among the independent variables in a regression model is due to the high correlations of a multiplicative function with its constituent variables, the multicollinearity can be greatly reduced by centering these variables around minimizing constants before forming the multiplicative function. The values of these constants that
Kent W. Smith, M.S. Sasaki
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, 2023 In this paper, I explore the symptoms of multicollinearity, detection methods for multiple linear regression models, examples using simulated and real-world data, and possible remedies.
Julian Jacklin (17715390)
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A Comparative Study of Multivariate Analysis Techniques for Highly Correlated Variable Identification and Management [PDF]
International Journal of Mathematical, Engineering and Management Sciences, 2020 In this work we attempt is to locate and analyze via multivariate analysis techniques, highly correlated covariates (factors) which are interrelated with the Gross Domestic Product and therefore are affecting either on short-term or on long-term its ...
K. Ntotsis +2 more
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On the Performance of Jackknife Based Estimators for Ridge Regression
IEEE Access, 2021 Regression techniques are generally used to predict a response variable using one or more predictor variables. In many fields of study, the regressors can be highly intercorrelated, which leads to the problem of multicollinearity.
Ismail Shah +5 more
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Cogent Social Sciences, 2021 The correctness and reliability of findings and\recommendations of empirical studies conducted by social and economic researchers depend largely on the efficiency of the econometrics methodologies employed in such studies. Of particular interest are such
Emmanuel A. Oduntan, J. O. Iyaniwura
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Remote Sensing, 2022 In recent years, geographically weighted regression (GWR) models have been widely used to address the spatial heterogeneity and spatial autocorrelation of PM2.5, but these studies have not fully considered the effects of all potential variables on PM2.5 ...
Youbing Tang +6 more
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The Impact of Government Expenditure Budget on Economic Growth In The Case Of Ethiopia [PDF]
Journal of Economic and Social Development (Varaždin), 2022 The paper examines the impact of government expenditures on economic growth in Ethiopia based on annual time series data from 1991 to 2016, gathered from the Ethiopian Ministry of Finance and World Bank databases.
Samuel Atsibha Gebreyesus
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A New Quantile-Based Approach for LASSO Estimation
Mathematics, 2023 Regularization regression techniques are widely used to overcome a model’s parameter estimation problem in the presence of multicollinearity. Several biased techniques are available in the literature, including ridge, Least Angle Shrinkage Selection ...
Ismail Shah +4 more
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