Apr 8, 2025 · I'm aware that one of SHAP's disadvantages is the precision of SHAP values in scenarios with multicollinearity because of the assumption of predictor independence.

Jun 23, 2020 · The difference between a linear mixed effects model and a linear model is that the former has random effects and the latter does not. Both have fixed effects and the same …

Jul 28, 2014 · Multicollinearity is a property of the regressors, not the model, so you don't need to look for "multicollinearity in GLM" as opposed, say, to "multicollinearity in OLS". In addition, …

Jul 16, 2020 · I am trying to conduct an ordinal logistic regression, but I first want to test if I fulfill the assumption of no multicollinearity. All of my 8 independent variables are ordinal with up to …

Say I have a set of time series data spanning 2000-2016 I code my years as the variable time, starting in 2000 as 0, 1, 2,....15 Say I want to compare the bush presidency to the obama …

17 I am currently assessing multicollinearity in my datasets. What threshold values of VIF and condition index below/above suggest a problem? VIF: I have heard that VIF $\geq 10$ is a …

multicollinearity covariance-matrix multivariate-normal-distribution See similar questions with these tags.

Apr 23, 2018 · Using SPSS, I have determined that for each of my IVs the VIF is between 1.2 to 5.1. How do I know what is a good cut off and what is a bad one? How is this determined? Is it …

Jan 29, 2017 · This UCLA page covers this issue in the context of logistic regression. But the phrasing of this question suggests some misunderstanding of multicollinearity and condition …

Nov 30, 2016 · I am building a Cox model containing around 8 variables. Two of the variables that are different measures of the same thing. Consequently, they are correlated with each other. …