A Picture's Worth a Thousand Words: Visualizing n-dimensional Overlap in Logistic Regression Models with Empirical Likelihood

• URL: http://arxiv.org/abs/2011.07614v1
• Date: Sun, 15 Nov 2020 19:39:56 GMT
• Title: A Picture's Worth a Thousand Words: Visualizing n-dimensional Overlap in Logistic Regression Models with Empirical Likelihood
• Authors: Paul A. Roediger
• Abstract summary: We introduce a sensitivity testing point of view for the maximum likelihood estimate for multidimensional predictor. The well known condition of Silvapulle is translated to be an empirical likelihood which, with existing R code, mechanizes the process of assessing overlap status. The code is applied to reveal the character of overlap by examining minimal overlapping structures and cataloging them in dimensions fewer than four.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this note, conditions for the existence and uniqueness of the maximum likelihood estimate for multidimensional predictor, binary response models are introduced from a sensitivity testing point of view. The well known condition of Silvapulle is translated to be an empirical likelihood maximization which, with existing R code, mechanizes the process of assessing overlap status. The translation shifts the meaning of overlap, defined by geometrical properties of the two-predictor groups, from the intersection of their convex cones is non-empty to the more understandable requirement that the convex hull of their differences contains zero. The code is applied to reveal the character of overlap by examining minimal overlapping structures and cataloging them in dimensions fewer than four. Rules to generate minimal higher dimensional structures which account for overlap are provided. Supplementary materials are available online.

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