Continuous dependence on parameters of certain self-affine measures, and their singularity

Ding, Daoxin

doi:10.1007/s10587-011-0068-0

Cited by 2 publications

(1 citation statement)

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“…Generally, there are three categories by age: below 14, 14-65, 65 above (65 inclusive) (Zhang and Lei 2011). There is a fact we cannot ignore that china is a large country, so there are total different economic development among area, especially the east and west part of China (Bao 2012;Kang 2009;Ding 2012). Each area, which of the age structure is changing from middle adult to senior citizen and this gap expends gradually.…”

Section: Assessment Of Real Casementioning

confidence: 99%

Evaluation of Population Age Structure Model Using Grey Clustering Theory

Bao-ping

2013

The 19th International Conference on Industrial Engineering and Engineering Management

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Due to the differences in economic development level and fertility policy enforcement, the population age structures are quite diverse from different regions in China. Thirty one provinces and Autonomous regions are used as clustering objects. Population from various age groups and Total dependency ratio are chosen as clustering indexes and whitening weight functions from corresponding indexes are defined. Meanwhile, clustering models of the population age structure assessment are provided and corresponding algorithms are compiled. Thirty one regions are divided into three groups by the age structure: excellent, normal, and poor. Then, sort again within each group to further analyze the reasons. The conclusion from our research provided a systematic, objective and accurate evaluation of age structure in various regions, it may has some reference value for solving China's population problems.

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Section: Assessment Of Real Casementioning

confidence: 99%