Unified targeted regularization framework for causal effect estimation with EDF outcomes using neural networks that jointly estimate outcome model, propensity scores, and fluctuation parameter.
(2023).Statistical Foundations of Actuarial Learning and its Applica- tions.Springer Actuarial.https://link.springer.com/book/10.1007/978-3-031-12409-9 15
4 Pith papers cite this work. Polarity classification is still indexing.
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CNN-LSTM and GNN-LSTM models added to a Lee-Carter baseline reduce test MSE by about 24% versus MortFCNet on French regional mortality data from 1990-2019, with largest gains at oldest ages.
Constrained GLM fitting is a superior method for enforcing the balance property in fitted insurance pricing models compared to two prior correction approaches, with links to ex-post risk sharing.
Boosting trees test necessary conditions for calibration and auto-calibration of regression models, shown powerful on a large insurance dataset.
citing papers explorer
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Climate-Driven Mortality Forecasting Using Deep Learning
CNN-LSTM and GNN-LSTM models added to a Lee-Carter baseline reduce test MSE by about 24% versus MortFCNet on French regional mortality data from 1990-2019, with largest gains at oldest ages.
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The Balance Property: The Constrained Case, with a View on Risk Sharing
Constrained GLM fitting is a superior method for enforcing the balance property in fitted insurance pricing models compared to two prior correction approaches, with links to ex-post risk sharing.
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Assessing model calibration with boosting trees
Boosting trees test necessary conditions for calibration and auto-calibration of regression models, shown powerful on a large insurance dataset.