Abstract
Longevity improvements have traditionally been analysed and extrapolated for future actuarial projections of longevity risk by using a range of statistical methods with different combinations of statistical data types. These meth-ods have shown great performances in explaining the trend movements of the longevity rate. However, actuaries believe that knowing the trend movements is not enough, especially in controlling the impact of the longevity risk. Accessing the effects of each level of the risk factors, especially ordinal risk factors, towards the improvements of the longevity rate would provide significant additional knowledge to the trend movements. Therefore, this study was conducted to determine the potentiality of Proportional-Odds Logistics Regression in ranking the levels of the ordinal risk factors based on their effects on longevity improvements. Based on the results, this method has successfully reordered the levels of each risk factor to be according to their effects in improving longevity rate. Hence, a more meaningful ranking system has been developed based on these new ordered risk factors. This new ranking system will help in improving the ability of any statistical methods in projecting the longevity risk when handling ordinal variables.
Highlights
Longevity improvements indicate a good sign that people are enjoying better fitness and better health conditions than the previous generations
This study was conducted to determine the potentiality of Proportional-Odds Logistics Regression in ranking the levels of the ordinal risk factors based on their effects on longevity improvements
A more meaningful ranking system can be developed based on these new ordered risk factors. This new ranking system will help in improving the ability of any statistical methods in projecting the longevity risk when handling ordinal variables
Summary
Longevity improvements indicate a good sign that people are enjoying better fitness and better health conditions than the previous generations. Some of the models involved application of life tables [6][7], stochastic mortality models [8], generalized dynamic factor models with vine-copulae simulations [9]; and various data mining techniques including logistic regression technique and decision tree technique [10][11] Policymakers find these statistical models to be more appealing to them because they help in improving the underwriting process by providing analytics-based approaches using readily accessible customers information to yield a more accurate, consistent, and efficient decision. Conclusion and recommendations are included in the last section
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