Abstract
A robust multilevel functional data method is proposed to forecast age-specific mortality rate and life expectancy for two or more populations in developed countries with high-quality vital registration systems. It uses a robust multilevel functional principal component analysis of aggregate and population-specific data to extract the common trend and population-specific residual trend among populations. This method is applied to age- and sex-specific mortality rate and life expectancy for the United Kingdom from 1922 to 2011, and its forecast accuracy is then further compared with standard multilevel functional data method. For forecasting both age-specific mortality and life expectancy, the robust multilevel functional data method produces more accurate point and interval forecasts than the standard multilevel functional data method in the presence of outliers.
Original language | English |
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Title of host publication | Recent advances in robust statistics |
Subtitle of host publication | theory and applications |
Editors | Claudio Agostinelli, Ayanendranath Basu, Peter Filzmoser, Diganta Mukherjee |
Place of Publication | New Delhi |
Publisher | Springer |
Pages | 169-184 |
Number of pages | 16 |
ISBN (Electronic) | 9788132236436 |
ISBN (Print) | 9788132236412 |
DOIs | |
Publication status | Published - 2016 |
Externally published | Yes |
Event | International Conference on Robust Statistics (ICORS) 2015 - Kolkata, India Duration: 12 Jan 2015 → 16 Jan 2015 |
Conference
Conference | International Conference on Robust Statistics (ICORS) 2015 |
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Country/Territory | India |
City | Kolkata |
Period | 12/01/15 → 16/01/15 |
Keywords
- Markov Chain Monte Carlo
- Forecast Error
- Prediction Interval
- Forecast Accuracy
- Functional Principal Component Analysis