Enhancing diagnostic of stochastic mortality models leveraging contrast trees: an application on Italian data
نویسندگان
چکیده
Abstract The rise in longevity the twentieth century has led to a growing interest modeling mortality, and new advanced techniques such as machine learning have recently joined more traditional models, Lee–Carter or Age Period Cohort. However, performances of these terms fitting observed data, are difficult compare unified framework. goodness-of-fit measures summarizing discrepancy between estimates from model values different for mortality models learning. We, therefore, employ technique, Contrast trees, which, leveraging on decision provides general approach evaluating quality fit kinds by detecting regions input space where work poorly. Once low-performance detected, we use boosting improve inaccuracies provided each model. To verify ability this approach, consider both standard stochastic algorithms estimate Italian rates Human Mortality Database. results discussed using graphical numerical tools, with particular attention high-error regions.
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ژورنال
عنوان ژورنال: Quality & Quantity
سال: 2023
ISSN: ['0033-5177', '1573-7845']
DOI: https://doi.org/10.1007/s11135-023-01711-x