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A holistic statistical approach for determining the relationships between social, economic and health markers using the English Longitudinal Study...

使用英语纵向研究确定社会、经济和健康指标之间关系的整体统计方法......

基本信息

批准号：
2035874
负责人：
金额：
--
依托单位：
Lancaster University
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2035874
关键词：
holistic statistical approach determining relationships

项目摘要

It has long been recognized that the demographics of developed societies such as Britain are undergoing a fundamental change, with people living longer lives, and as a consequence experiencing a longer period of frailty in old age. This has immediate implications for economy and labour force dynamics, as well as the social welfare system (Tinker, 2002). Increasing efforts are being made to improve our understanding of this new phenomenon. As the population, by definition, is a mixture of very heterogeneous entities, it is vital to characterise the varying experiences of ageing in order to understand the needs of individuals and to quantify the impacts of any future policy changes. In parallel, due to advancement in technology, there has been an explosive growth in data collection, which could be utilised to improve our understanding of the ageing process on a finer scale and to aid both individual and policy-level decision making. However, these technological advancements also bring new challenges to analyse such rich information, due to the problem known as the "curse of dimensionality". This states that any standard analysis becomes a non-trivial task in a high dimensional context (many observed variables). Additionally, even in the era of big data, it is impossible to collect all the relevant information that characterises individual variability, and variability and noise levels may even increase with increasing number of measurements.

人们早就认识到，像英国这样的发达社会的人口结构正在发生根本性的变化，人们的寿命更长，因此在老年时经历了更长的虚弱期。这对经济和劳动力动态以及社会福利制度产生了直接影响（Tinker，2002年）。目前正在作出越来越多的努力，以提高我们对这一新现象的认识。根据定义，人口是非常不同的实体的混合体，因此必须了解老龄化的不同经历，以便了解个人的需要，并量化未来任何政策变化的影响。与此同时，由于科技的进步，数据收集工作也有了爆炸性的增长，这些数据可以用来提高我们对老龄化过程的更精细的理解，并有助于个人和政策层面的决策。然而，这些技术进步也给分析如此丰富的信息带来了新的挑战，这是由于被称为“维数灾难”的问题。这表明任何标准分析在高维环境（许多观察变量）中都是一项重要的任务。此外，即使在大数据时代，也不可能收集表征个体变异性的所有相关信息，变异性和噪声水平甚至可能随着测量次数的增加而增加。