ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Filtering by
- All Subjects: Statistics
- Creators: Lanchier, Nicolas
munity. With life insurance policies and annuity products as dominant financial
instruments which depend on future mortality rates, there is a risk that observed
human mortality experiences will differ from projected when they are sold. From an
insurer’s portfolio perspective, to curb this risk, it is imperative that models of hu
man survivorship are constantly being updated and equipped to accurately gauge and
forecast mortality rates. At present, the majority of actuarial research in mortality
modeling involves factor-based approaches which operate at a global scale, placing
little attention on the determinants and interpretable risk factors of mortality, specif
ically from a spatial perspective. With an abundance of research being performed
in the field of spatial statistics and greater accessibility to localized mortality data,
there is a clear opportunity to extend the existing body of mortality literature to
wards the spatial domain. It is the objective of this dissertation to introduce these
new statistical approaches to equip the field of actuarial science to include geographic
space into the mortality modeling context.
First, this dissertation evaluates the underlying spatial patterns of mortality across
the United States, and introduces a spatial filtering methodology to generate latent
spatial patterns which capture the essence of these mortality rates in space. Second,
local modeling techniques are illustrated, and a multiscale geographically weighted
regression (MGWR) model is generated to describe the variation of mortality rates
across space in an interpretable manner which allows for the investigation of the
presence of spatial variability in the determinants of mortality. Third, techniques for
updating traditional mortality models are introduced, culminating in the development
of a model which addresses the relationship between space, economic growth, and
mortality. It is through these applications that this dissertation demonstrates the
utility in updating actuarial mortality models from a spatial perspective.