423. Finding robust profiles of mental well-being across Europe
Invited abstract in session MB-9: Data Science in Insurance and Finance: New perspectives and Applications, stream OR in Finance and Insurance .
Monday, 10:30-12:00Room: Clarendon SR 2.01
Authors (first author is the speaker)
| 1. | Irene Albarran
|
| Statistics, University Carlos III of Madrid |
Abstract
The aim of this work is to find robust profiles of older adults to better understand differing levels of emotional well-being across Europe. We use data from the Survey of Health, Ageing and Retirement in Europe, which was carried out in 26 countries and represents over 181 million aged individuals in Europe. In a first step, we use the information of around 80 variables in order to design composite indicators focused on quality of life in older age, loneliness, social integration and social connectedness. Next, we combine them with a collection of descriptive qualitative variables by computing pairwise robust generalized Gower distance for weighted data. Finally, we apply Fast k-medoids clustering algorithm to obtain the profiles. This new proposal can help to improve the identification of patterns and trends in insurance, for example in the development of more accurate predictive models to determine personalised premiums tailored to the specific needs of individuals. Pyhton packages PyDistances and FastKmedoids are used for the computations.
Key words: Emotional well-being, elderly, Fast k-medoids, robust G-Gower, weighted mixed-type data
Keywords
- Health Care
- Analytics and Data Science
- Risk Analysis and Management
Status: accepted
Back to the list of papers