I Are there Issues with Separation of Values in Ordinal Logistic Regression

[WWGD]

Hi all , just curious if someone knows of any issues of Separation of Points in Ordinal 3-valued
Logistic Regression. I think I have an idea of why there are issues with separation in binary
Logistic -- the need for the S-curve to go to 0 quickly makes the Bo term go to infinity. Are there
similar issues with 3-valued (or higher-valued) Logistic Regression?

 

[MarneMath]

I'm not entirely clear what you mean by "Separation of Points". Whenever I hear "Separation" with regards to logistic regression, it deals with complete separation or quasi separation, which tends to occur with small dataset/miscoded datasets. The problem that causes this (MLE not existing) doesn't disappear in more general cases.

There's ways around that (sometimes), but I feel that we may be talking about two different things.

 

[WWGD]

I'm not entirely clear what you mean by "Separation of Points". Whenever I hear "Separation" with regards to logistic regression, it deals with complete separation or quasi separation, which tends to occur with small dataset/miscoded datasets. The problem that causes this (MLE not existing) doesn't disappear in more general cases.

There's ways around that (sometimes), but I feel that we may be talking about two different things.

Hi thanks for replying. Separation happens when there is a value Xo of the independent variable (obviously this applies to cases with numerica; variables) such that for all X>Xo all trials (Bernoulli or multinomial) are fails or all trials are successes. e.g., if Y dependent was "has Cancer" and X is number of cigarettes smoked per week, then X is separated if for, e.g., X>10 all are fails, i.e., everyone who smoked more than 10 cigarettes got cancer.

 

[MarneMath]

Ok, then I think we are talking about he same thing. Then yes, separation is a problem even for higher orders. Most statistical packages are good at notifying you when this happens. One way around this is by using a penalizing the maximum estimator. I'm personally a fan of using a hidden logistic to overcome this when necessary.

 

[WWGD]

Just a followup on this: would it be reasonable, in the sense of not affecting "intrinsic" properties of a data set with separation of values with smallish size each, say in the range [0,5] , to slightly alter ; increase/decrease some of the data values , so as to overcome this issue, i.e., so that the values beyond a certain number are not monotone? Say my cutoff point for this data set within the [0,5] range is 3 and I have several points with value 3. Then I could change the data set to replace , in some cases, 3 by 3.02, in other cases 3 would be replaced by, say 2.98 , in order to avoid this problem? I just want to be able to model the probability of success by doing this; obviously, I would think, most of the properties of the data would be preserved by doing this?