Elo's heuristic and MLE perspectives coincide for binary logistic cases but demand closed-form noise corrections to scale and home-field parameters for accurate prediction, outperforming the standard approach and showing non-convergence in FIFA rankings.
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Fine-tuned transformers with multi-task learning recover substantial wording-derived signal for item difficulty at small sample sizes typical in applied testing.
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New insights into Elo algorithm for practitioners and statisticians
Elo's heuristic and MLE perspectives coincide for binary logistic cases but demand closed-form noise corrections to scale and home-field parameters for accurate prediction, outperforming the standard approach and showing non-convergence in FIFA rankings.
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Response-free item difficulty modelling for multiple-choice items with fine-tuned transformers: Component-wise representation and multi-task learning
Fine-tuned transformers with multi-task learning recover substantial wording-derived signal for item difficulty at small sample sizes typical in applied testing.