3
$\begingroup$

I am familiar with the dummy variable trap in normal OLS, in which we should include one less dummy variable than the total of categories to avoid the problem of multicollinearity.

However, I was wondering if it is also the case in duration models. Specifically, I am running a Weibull survival model with six dummy variables, exhaustive and mutually exclusive, and there is no sign of multicollinearity (Even if all the coefficients of the dummy variables are negative, which makes it a bit harder to interpret as I'm not sure what the dummies are measuring themselves against).

So the question is: does the dummy variable trap also occur in survival/duration models?

Improve this question
$\endgroup$
8
  • 3
    $\begingroup$ Should be the same as in OLS... $\endgroup$
    – user73432
    Apr 13, 2015 at 12:47
  • 1
    $\begingroup$ Are you also fitting an intercept? $\endgroup$
    – Scortchi - Reinstate Monica ♦
    Apr 13, 2015 at 15:03
  • $\begingroup$ Yes, there is also an intercept. Also the AIC is (slightly) higher when I exclude one of the dummy variables, which implies that the model fit is better with all the dummies. $\endgroup$
    – Not Fisher
    Apr 13, 2015 at 15:58
  • $\begingroup$ How are you determining that you don't have perfect collinearity in the design matrix? $\endgroup$
    – Scortchi - Reinstate Monica ♦
    Apr 13, 2015 at 16:06
  • 1
    $\begingroup$ Very trusting of you! Check the condition number of $X^\mathrm{T}X$ (where $X$ is the design matrix). Or try changing the order in which coefficients are input to the model (I've just experimented with the survfit function in R - fitting a Weibull model when the design matrix isn't full rank also gives one of the infinity of possible solutions without warning; it gives different solutions when you vary the order of the predictors). $\endgroup$
    – Scortchi - Reinstate Monica ♦
    Apr 13, 2015 at 16:39

1 Answer 1

Reset to default
0
$\begingroup$

Dummy Variable trap usually occurs in proportional hazards models/semi-parametric models very well. and yes! you should check significant improvement of each dummy variable you add.

Improve this answer
$\endgroup$
5
  • $\begingroup$ But why can it also occur in these models? And what of the OP's assertion that in their model there isn't perfect collinearity in the model matrix? $\endgroup$
    – Scortchi - Reinstate Monica ♦
    Apr 13, 2015 at 15:06
  • $\begingroup$ I studied these models are good to test/judge by dummy variables & Yes! Semi-parametric is good for such. $\endgroup$
    – Hemant Rupani
    Apr 13, 2015 at 15:14
  • $\begingroup$ As @Scortchi said, there is no multicollinearity in the regression output, so that's why I'm not exactly sure it occurs in survival models... $\endgroup$
    – Not Fisher
    Apr 13, 2015 at 15:56
  • $\begingroup$ You know Cox hazard model. $\endgroup$
    – Hemant Rupani
    Apr 13, 2015 at 16:26
  • $\begingroup$ Very strange second part of the answer. $\endgroup$
    – Michael M
    Jan 3 at 16:13

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.