Detecting and Correcting for Separation in Strategic Choice Models

Crisman-Cox, Casey; Gasparyan, Olga; Signorino, Curtis S.

doi:10.1017/pan.2022.36

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Both methods ensure finite estimates in theory and usually produce reasonably sized estimates in practice. Methodologists continue to recommend these penalized or Bayesian estimators as a solution to separation (e.g., Anderson, Bagozzi, and Koren 2021;Cook, Hays, and Franzese 2020;Cook, Niehaus, and Zuhlke 2018;Crisman-Cox, Gasparyan, and Signorino 2023).…”

Section: Introductionmentioning

confidence: 99%

Hypothesis Tests under Separation

Rainey

2023

Polit. Anal.

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Separation commonly occurs in political science, usually when a binary explanatory variable perfectly predicts a binary outcome. In these situations, methodologists often recommend penalized maximum likelihood or Bayesian estimation. But researchers might struggle to identify an appropriate penalty or prior distribution. Fortunately, I show that researchers can easily test hypotheses about the model coefficients with standard frequentist tools. While the popular Wald test produces misleading (even nonsensical) p-values under separation, I show that likelihood ratio tests and score tests behave in the usual manner. Therefore, researchers can produce meaningful p-values with standard frequentist tools under separation without the use of penalties or prior information.

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Section: Introductionmentioning

confidence: 99%

Hypothesis Tests under Separation

Rainey

2023

Polit. Anal.

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Binary classification with fuzzy logistic regression under class imbalance and complete separation in clinical studies

Charizanos,

Demirhan,

İçen

2024

BMC Med Res Methodol

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Background In binary classification for clinical studies, an imbalanced distribution of cases to classes and an extreme association level between the binary dependent variable and a subset of independent variables can create significant classification problems. These crucial issues, namely class imbalance and complete separation, lead to classification inaccuracy and biased results in clinical studies. Method To deal with class imbalance and complete separation problems, we propose using a fuzzy logistic regression framework for binary classification. Fuzzy logistic regression incorporates combinations of triangular fuzzy numbers for the coefficients, inputs, and outputs and produces crisp classification results. The fuzzy logistic regression framework shows strong classification performance due to fuzzy logic’s better handling of imbalance and separation issues. Hence, classification accuracy is improved, mitigating the risk of misclassified conditions and biased insights for clinical study patients. Results The performance of the fuzzy logistic regression model is assessed on twelve binary classification problems with clinical datasets. The model has consistently high sensitivity, specificity, F1, precision, and Mathew’s correlation coefficient scores across all clinical datasets. There is no evidence of impact from the imbalance or separation that exists in the datasets. Furthermore, we compare the fuzzy logistic regression classification performance against two versions of classical logistic regression and six different benchmark sources in the literature. These six sources provide a total of ten different proposed methodologies, and the comparison occurs by calculating the same set of classification performance scores for each method. Either imbalance or separation impacts seven out of ten methodologies. The remaining three produce better classification performance in their respective clinical studies. However, these are all outperformed by the fuzzy logistic regression framework. Conclusion Fuzzy logistic regression showcases strong performance against imbalance and separation, providing accurate predictions and, hence, informative insights for classifying patients in clinical studies.

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Detecting and Correcting for Separation in Strategic Choice Models

Cited by 2 publications

References 18 publications

Hypothesis Tests under Separation

Hypothesis Tests under Separation

Binary classification with fuzzy logistic regression under class imbalance and complete separation in clinical studies

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