Backtesting stochastic mortality models by prediction interval-based metrics

The rise in longevity in the twentieth century has led to a growing interest in modeling mortality, and new advanced techniques such as machine learning have recently joined to more traditional models, such as the Lee–Carter or the Age Period Cohort. However, the performances of these models, in terms of fitting to the observed data, are difficult to compare in a unified framework. The goodness-of-fit measures summarizing the discrepancy between the estimates from the model and the observed values are different for traditional mortality models and machine learning. We, therefore, employ a new technique, Contrast trees, which, leveraging on decision trees, provides a general approach for evaluating the quality of fit of different kinds of models by detecting the regions in the input space where models work poorly. Once the low-performance regions are detected, we use Contrast boosting to improve the inaccuracies of mortality estimates provided by each model. To verify the ability of this approach, we consider both standard stochastic mortality models and machine learning algorithms in the estimate of the Italian mortality rates from the Human Mortality Database. The results are discussed using both graphical and numerical tools, with particular attention to the high-error regions.

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Biosafety management strategy based on multiple linear regression model

Zhao

Xiao

Zhang

et al. 2023

Applied Mathematics and Nonlinear Sciences

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With the rise of artificial intelligence technology in the context of digital development of big data and information, the biosafety management method under the traditional model can hardly fit the development requirements of the times, and the biosafety management under the traditional model has various problems such as slow manual efficiency, tedious management work, and humanization of work. This paper focuses on studying a biosafety management strategy based on a multiple regression model, then analyzes and compares the two methods of the one-dimensional linear regression model and the multiple linear regression model. The study is conducted under the three indicators of biosafety management weight score. Arithmetic means to score and significance of 100 enterprises (schools, pharmaceutical companies, hospitals, and other types of these four major types) under the multiple linear regression model. The results show schools topped the list with a biosafety management weight score of 0.793. The arithmetic mean scores of the four categories of enterprises were ranked in descending order: schools, hospitals, pharmaceutical plants, and other categories. The size of the enterprise has a significant and positive effect on biosafety management, which means that the larger the size of the enterprise, the more resources the enterprise has under its control, and then the implementation of biosafety management and the improvement of its performance will be guaranteed. Analyzing the efficiency of biosafety management in hospitals based on three different methods over the past 20 years, it was found that the efficiency of biosafety management based on multiple linear regression models fluctuated from 60% to 95% compared to the other two methods. This study promotes university laboratories as important and safe bases for society to cultivate highly qualified and skilled personnel in line with the new era of production, management, and service lines and to contribute to the country’s experimental research work. This study has substantial reference value for biosafety management and promotes bioscience development in China.

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Backtesting stochastic mortality models by prediction interval-based metrics

Cited by 3 publications

References 39 publications

Population Ageing Process and Depopulation Context in Western Balkans

Population Ageing Process and Depopulation Context in Western Balkans

Enhancing diagnostic of stochastic mortality models leveraging contrast trees: an application on Italian data

Biosafety management strategy based on multiple linear regression model

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