This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The maximum likelihood estimation (MLE) method, typically used for polytomous logistic regression, is prone to bias due to both misclassification in outcome and contamination in the design matrix. Hence, robust estimators are needed. In this study, we propose such a method for nominal response data with continuous covariates. A generalized method of weighted moments (GMWM) approach is developed for dealing with contaminated polytomous response data. In this approach, distances are calculated based on individual sample moments. And Huber weights are applied to those observations with large distances. Mellow-type weights are also used to downplay leverage points. We describe theoretical properties of the proposed approach. Simulations suggest that the GMWM performs very well in correcting contamination-caused biases. An empirical application of the GMWM estimator on data from a survet demonstrates its usefulness.
"Following" is like subscribing to any updates related to a preprint.
These updates will appear in your home dashboard each time you visit PeerJ.
You can also choose to receive updates via daily or weekly email digests.
If you are following multiple preprints then we will send you
no more than one email per day or week based on your preferences.
Note: You are now also subscribed to the subject areas of this preprint
and will receive updates in the daily or weekly email digests if turned on.
You can add specific subject areas through your profile settings.