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Apr 13, 2017 at 12:44 history edited CommunityBot
replaced http://stats.stackexchange.com/ with https://stats.stackexchange.com/
Jul 8, 2015 at 9:44 vote accept James White
Jul 8, 2015 at 9:44 vote accept James White
Jul 8, 2015 at 9:44
Jul 7, 2015 at 19:23 history tweeted twitter.com/#!/StackStats/status/618500622778826752
Jul 7, 2015 at 17:37 history edited EdM
added appropriate model-selection tag
Jul 7, 2015 at 17:35 answer added EdM timeline score: 2
Jul 7, 2015 at 15:18 comment added James White Indeed I will be experiencing high levels of collinearity with some of the variables being derived from similar calculations. This was intended to be an alternative way of reaching a final set of candidate variables by exploring their individual significance against the response variable, with the PCA approach being shown to exclude some of the important ones. Following the ranking of each model by the AIC score, I was going to validate the assumptions and then test for colinearity before putting these into the final model as in Method 1. Is this not wise? Thank you for the comment!
Jul 7, 2015 at 14:54 comment added EdM Be careful when you try to "explore the significance of each explanatory variable individually". Some of your >100 explanatory variables are likely to be inter-correlated, so their individual levels of "significance" in this data sample might be substantially different from a different sample. PCA helps mask that problem, as correlated variables show up in similar principal components. So you might want to consider regularization methods instead.
Jul 7, 2015 at 14:39 vote accept James White
Jul 8, 2015 at 9:44
Jul 7, 2015 at 14:39 vote accept James White
Jul 7, 2015 at 14:39
Jul 7, 2015 at 14:39 vote accept James White
Jul 7, 2015 at 14:39
Jul 7, 2015 at 14:34 answer added Avraham timeline score: 2
Jul 7, 2015 at 13:54 answer added dsaxton timeline score: 3
Jul 7, 2015 at 13:30 review First posts
Jul 7, 2015 at 14:12
Jul 7, 2015 at 13:29 history asked James White CC BY-SA 3.0