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AuthorGenz, Martindc.contributor.author
Date of accession2019-09-03T12:01:53Zdc.date.accessioned
Available in OPARU since2019-09-03T12:01:53Zdc.date.available
Year of creation2019dc.date.created
Date of first publication2019-09-03dc.date.issued
AbstractA variety of literature addresses the question of how the age distribution of deaths changes over time as life expectancy increases. However, corresponding terms such as extension, compression, or rectangularization are sometimes defined only vaguely, and statistics used to detect certain scenarios can be misleading. The matter is further complicated because mixed scenarios can prevail, and the considered age range can have an impact on observed mortality patterns. In this thesis, we establish a unique classification framework for realized mortality scenarios that allows for the detection of both pure and mixed scenarios. The framework not only can test the presence of a particular scenario but also can assign a unique scenario to any observed mortality evolution. Furthermore, it can detect different mortality scenarios for different age ranges in the same population. We apply the framework to mortality data for different countries all over the world, both sexes, and different age ranges. This yields a complete picture of historical mortality evolution patterns in those countries and adds to existing analyses where only certain aspects of mortality evolution patterns have been considered. We discuss similarities and differences in the historical mortality evolution patterns between different populations, sexes, and age ranges. We particularly focus on the estimation of the right endpoint of the distribution of human lifetimes and apply methods of the extreme value theory for the estimation of this age. To this end we use combined mortality data from two different sources of mortality data. The use of old-age mortality data typically raises two issues: sparse information on the old ages and censored observations. We address this by combining methods of the censored extreme value theory with sub-sampling and cross-validation techniques. To project future mortality a variety of mortality models can be used. However, the parameters of most of these models lack a clear demographic interpretation. On the other hand, demographers make predictions on future mortality but typically focus on single aspects only instead of comprehensive mortality forecasts. The final part of this thesis aims to close the gap between these forecasting approaches. To this end, we establish a new best estimate mortality model which is based on the extrapolation of the four statistics of the classification framework we present in the first part of this thesis. We show that our model yields forecasts for the deaths curve which are consistent with the most recent demographic trends in the deaths curve evolution. Moreover, expert opinions on future trends in the mortality evolution can easily be incorporated, and we illustrate how the model can be used for scenario analyses.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
Articles in publ.Börger, M., Genz, M., and Ruß, J. (2018). Extension, Compression, and Beyond – A Unique Classification System for Mortality Evolution Patterns. Demography, 55 (4): 1343-1361. DOI: 10.1007/s13524-018-0694-3.dc.relation.haspart
Articles in publ.Genz, M. (2017). A Comprehensive Analysis of the Patterns of Worldwide Mortality Evolution. 2017 Living to 100 Society of Actuaries International Symposium Monograph. URL: https://www.soa.org/essays-monographs/2017-living-to-100/2017-living-100-monograph-genz-paper.pdf.dc.relation.haspart
LicenseStandarddc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v3dc.rights.uri
KeywordMortality scenario classificationdc.subject
KeywordCensoringdc.subject
KeywordOld-age mortalitydc.subject
KeywordDemographic mortality trendsdc.subject
KeywordDemographic scenario analysesdc.subject
Keyword(Consistent) Mortality forecastingdc.subject
Dewey Decimal GroupDDC 510 / Mathematicsdc.subject.ddc
Dewey Decimal GroupDDC 570 / Life sciencesdc.subject.ddc
LCSHCensored observations (Statistics)dc.subject.lcsh
LCSHExtreme value theorydc.subject.lcsh
LCSHDeath; Causes; Classificationdc.subject.lcsh
LCSHMortality; Forecasting; Mathematical modelsdc.subject.lcsh
TitleThe past and the future of mortalitydc.title
Resource typeDissertationdc.type
Date of acceptance2019-07-19dcterms.dateAccepted
RefereeChen, Andc.contributor.referee
RefereeZwiesler, Hans-Joachimdc.contributor.referee
DOIhttp://dx.doi.org/10.18725/OPARU-18420dc.identifier.doi
PPN1676003428dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-18477-1dc.identifier.urn
GNDZensierte Stichprobedc.subject.gnd
GNDGrenzwert (Mathematik)dc.subject.gnd
GNDSzenariodc.subject.gnd
GNDBevölkerungsentwicklungdc.subject.gnd
GNDSterbezifferdc.subject.gnd
FacultyFakultät für Mathematik und Wirtschaftswissenschaftenuulm.affiliationGeneral
InstitutionInstitut für Versicherungswissenschaftenuulm.affiliationSpecific
Grantor of degreeFakultät für Mathematik und Wirtschaftswissenschaftenuulm.thesisGrantor
DCMI TypeTextuulm.typeDCMI
CategoryPublikationenuulm.category
University Bibliographyjauulm.unibibliographie


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