Prediction of mortality rates using augmented data

Tan Chon Sern, and Pooi, Ah Hin * (2016) Prediction of mortality rates using augmented data. Jurnal Teknologi, 78 (4). pp. 19-23. ISSN 2180–3722

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Prediction of future mortality rate is of significant priority in the insurance industry today as insurers face challenging tasks in providing retirement benefits to a population with increasing life expectancy. A time series model based on multivariate power-normal distribution has been used in the literature on the United States (US) mortality data in the years 1933 to 2000 to predict the future mortality rates in the years 2001 to 2010. To improve the predictive ability, the US mortality data is augmented to include more variables such as death rates by gender and death rates of other countries with similar demographics. Apart from having good ability to cover the observed future mortality rate, the prediction intervals based on the augmented data performed better because they also tend to have shorter interval lengths.

Item Type: Article
Additional Information: First author is with Department of Mathematical and Actuarial Sciences, Universiti Tunku Abdul Rahman, Malaysia.
Uncontrolled Keywords: Death rates; power-normal distribution; prediction interval; time series model
Subjects: H Social Sciences > HA Statistics
Divisions: Others > Non Sunway Academics
Sunway University > School of Mathematical Sciences > Department of Applied Statistics
Depositing User: Dr Janaki Sinnasamy
Date Deposited: 20 Dec 2016 09:25
Last Modified: 14 May 2019 07:46

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