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3 articles for “Multicollinearity”
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Effect of Image Band Number and Band Multicollinearity on Mapping Accuracy of Tree Diversity and Evenness
Abstract: 800x600 Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 Satellite imageries are increasingly being used in the studies of plant diversity due to cost effective wider spatial coverage. Multispectral or hyperspectral imageries consist of different spectral bands where some bands might be multicollineared among them and deemed redundant to use in the classification/analysis. This study investigates how the accuracies of maps of tree diversity and evenness of an area …
Published in Journal of Remote Sensing & GIS · Vol. 4, Issue 2, 2013 · pp. 14–22 Read article
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Performance of Some Modified Ordinary Ridge Regression Estimators
Abstract: In multiple linear regressions, if the data suffer from severe multicollinearity, then the ordinary least squares (OLS) method become more sensitive to it, and in such a case OLS could yield wrong sign for some of the regression coefficients. Therefore, when such a situation arises, we could use one of the biased regression methods viz., ridge regression, principal component regression, and so on, as an alternative method to OLS. This …
Published in Research & Reviews : Journal of Statistics Read article
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Neural Network Machine Learning Analysis for Noisy Data: R Programming
Abstract: Abstract: This review paper clearly discusses the compression between Neural Network Machine Learning Analysis for Noisy Data: R Programming. Although there is large gap between data analysis to analyze overfitting and multicollinearity problems in data sets. Its primary purpose is to explain the machine learning procedures using neural network whose data structure were cross validation using R software whose outputs were sufficiently explain with various intermediate output and graphical interpretation …
Published in Recent Trends in Programming languages · Vol. 6, Issue 3, 2019 · pp. 1–10 Read article