Results 1 to 10 of about 300,687 (184)
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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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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LASSO Regression in Consumer Price Index Malaysia [PDF]
, 2021 This study is aimed to determine the factors contributing to the prediction of the total Consumer Price Index (CPI) in Malaysia through model selection using LASSO regression.
Mohd Padzil, Siti Aisyah +2 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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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.
Jireh Yi-Le Chan +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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Multicollinearity and Model Misspecification
Sociological Science, 2016 Multicollinearity in linear regression is typically thought of as a problem of large standard errors due to near-linear dependencies among independent variables. This problem can be solved by more informative data, possibly in the form of a larger sample.
Christopher Winship, Bruce Western
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Jurnal Biometrika dan Kependudukan, 2018 Correlation between independent variables in multiple linear regression model called multicollinearity. One of the assumptions of multiple linear regression free from multicollinearity problem. Principal Component Analysis (PCA) method in this study aims
Fita Mega Kusuma, Arief Wibowo
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