Linear Regression Analysis: Theory and Computing

By Xin Yan, Xiaogang Su. 2009

843 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/433bc03ebf8f90c96f631a9459fdfa7c003818f4

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Citations in the corpus

On The Stability of Interpretable Models (2018, methodology): “We focus on three algorithms for linear models: Linear Regression (LINREG) [26], and its regularized forms RIDGE [27] and LASSO [28], as implemented by the scikit-learn library.”
Organizational identification, work engagement, and job satisfaction (2015, methodology): “Prior to the analyses, all variables used in the models were checked for multicollinearity (by examining the Variation Inflation Factors) and no issues were detected since all VIF values were 5 and not beyond the threshold of 10 (Yan & Su, 2009).”
Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data. (2017, background): “(3) is one of linear regression for which there are well-established techniques 29 .”
Big cats kill more livestock when wild prey reaches a minimum threshold (2015, methodology): “We excluded outlier predictorswith Cook's distance ~1 and higher andmulticollinear predictorswith significant Spearman's correlation coefficient rs (Yan and Su, 2009).”
Interpretable Machine Learning in Healthcare (2018, methodology): “models like decision trees [24], regression models [33], and context explanation networks [3] the explanation itself is part of the model.”
Machine learning in concrete strength simulations: Multi-nation data analytics (2014, methodology): “The LR model applies four regression methods using ordinary least squares estimation: enter, stepwise, forward, and backward [49].”