IRSL applies IRT to reduce scaling law estimation from O(M×N) to O(M+N) parameters, enabling reliable estimates with only 50 questions per benchmark after calibration and generalizable ability scores across related benchmarks.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Compares Markov chain, beta regression and multinomial logistic regression for loan default term-structures on mortgage data and reports successive outperformance plus new diagnostics.
citing papers explorer
-
Item Response Scaling Laws: A Measurement Theory Approach for Efficient and Generalizable Neural Scaling Estimation
IRSL applies IRT to reduce scaling law estimation from O(M×N) to O(M+N) parameters, enabling reliable estimates with only 50 questions per benchmark after calibration and generalizable ability scores across related benchmarks.
-
Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework
Compares Markov chain, beta regression and multinomial logistic regression for loan default term-structures on mortgage data and reports successive outperformance plus new diagnostics.