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Robust approach to life expectancy projection

Majewska, Justyna; Trzpiot, Grazyna


Mortality models most often are used to make projections of life expectancy. A good mortality model should satisfy some desirability criteria (Cairns et al (2008)). Models should be robust which means that parameter uncertainty should be low and small changes in the data should not result in significant changes in the estimates of the parameters and in their interpretation. Most of the existing mortality models are not robust against outliers due to wars, pandemics etc. or so called "longevity outliers". This paper is not the first attempt to deal with outliers in mortality data. Hyndman and Ullah (2007) used a combination of robust, nonparametric statistics and functional data analysis in developing a method for projection of age-specific mortality rates observed over time. While their objective was to identify and remove outliers, we highlight the necessity of incorporating them into projections in order to capture, in a more realistically way, perturbations that may occur in the future. The main contribution of this paper is to utilize a highly robust estimator to minimize the effect of outliers on point forecasts of life expectancy. ... mehr

Verlagsausgabe §
DOI: 10.5445/KSP/1000058749/26
Veröffentlicht am 30.01.2018
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Wirtschaftswissenschaften – Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2017
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000079733
Erschienen in Archives of Data Science, Series A (Online First)
Band 2
Heft 1
Seiten 14 S. online
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