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 …

Mar 13, 2015 · In my understanding, highly correlated variables won't cause multi-collinearity issues in random forest model (Please correct me if I'm wrong). However, on the other way, if I …

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. …

Feb 5, 2021 · multicollinearity fixed-effects-model Improve this question edited Feb 10, 2021 at 21:48 asked Feb 5, 2021 at 19:10

Multicollinearity is the symptom of that lack of useful data, and multivariate regression is the (imperfect) cure. Yet so many people seem to think of multicollinearity as something they're …

Jul 15, 2021 · 12 Does it ever make sense to check for multicollinearity and perhaps remove highly correlated variables from your dataset prior to running LASSO regression to perform …

Feb 10, 2016 · I have 15 variables some of which are highly correlated. I want to run a cointegration test in the ARDL and VAR/VECM frameworks. Due to the correlation …

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. This …

Aug 23, 2017 · You need to consider more than just linear correlations among covariates when working with GAMs. You need to consider nonlinear correlations or dependencies. This is …

Nov 3, 2022 · Checking multicollinearity with generalized additive model in R Ask Question Asked 7 years ago Modified 2 years, 11 months ago