A nonparametric random coefficient approach for life expectancy growth using a hierarchical mixture likelihood model with application to regional data from North Rhine-Westphalia (Germany)

Background Life expectancy is of increasing prime interest for a variety of reasons. In many countries, life expectancy is growing linearly, without any indication of reaching a limit. The state of North Rhine-Westphalia (NRW) in Germany with its 54 districts is considered here where the above ment...

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Main Authors: Böhning, Dankmar (Author), Karasek, Sarah (Author), Terschüren, Claudia (Author), Annuß, Rolf (Author), Fehr, Rainer (Author)
Format: Article
Language:English
Published: 2013-03-09.
Subjects:
Online Access:Get fulltext
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042 |a dc 
100 1 0 |a Böhning, Dankmar  |e author 
700 1 0 |a Karasek, Sarah  |e author 
700 1 0 |a Terschüren, Claudia  |e author 
700 1 0 |a Annuß, Rolf  |e author 
700 1 0 |a Fehr, Rainer  |e author 
245 0 0 |a A nonparametric random coefficient approach for life expectancy growth using a hierarchical mixture likelihood model with application to regional data from North Rhine-Westphalia (Germany) 
260 |c 2013-03-09. 
856 |z Get fulltext  |u https://eprints.soton.ac.uk/350653/1/1471-2288-13-36.pdf 
520 |a Background Life expectancy is of increasing prime interest for a variety of reasons. In many countries, life expectancy is growing linearly, without any indication of reaching a limit. The state of North Rhine-Westphalia (NRW) in Germany with its 54 districts is considered here where the above mentioned growth in life expectancy is occurring as well. However, there is also empirical evidence that life expectancy is not growing linearly at the same level for different regions. Methods To explore this situation further a likelihood-based cluster analysis is suggested and performed. The modelling uses a nonparametric mixture approach for the latent random effect. Maximum likelihood estimates are determined by means of the EM algorithm and the number of components in the mixture model are found on the basis of the Bayesian Information Criterion. Regions are classified into the mixture components (clusters) using the maximum posterior allocation rule. Results For the data analyzed here, 7 components are found with a spatial concentration of lower life expectancy levels in a centre of NRW, formerly an enormous conglomerate of heavy industry, still the most densely populated area with Gelsenkirchen having the lowest level of life expectancy growth for both genders. The paper offers some explanations for this fact including demographic and socio-economic sources. Conclusions This case study shows that life expectancy growth is widely linear, but it might occur on different levels. 
540 |a other 
655 7 |a Article