Analysis of survery data

研究建议涉及在时间或空间上观察之间的关联和依赖性的统计建模，特别强调调查数据和调查方法。我建议从三个方面对复杂的调查数据进行分析。第一个主题是事件时间联合分布和空间分析的密度估计；当我们对评估一种类型的事件是否可能触发另一种事件感兴趣时，前者是相关的，后者将适用于疾病发病率空间分布的可视化。第二个主题是多层模型中的估计，其中使用多个聚合级别的解释变量来模拟响应；由于这种建模越来越广泛地使用，因此将探索允许在估计方差成分时进行复杂抽样设计的重抽样方法，并将其提供给分析人员。最后，我将研究层次模型的应用，以解释调查数据分析中的人口异质性，特别是跨文化调查方法的应用。在国际调查的背景下，这个话题引起了社会科学家和卫生科学家的极大兴趣。

The research proposal concerns the statistical modelling of association and dependence among observations in time or space, with particular emphasis on survey data and survey methodology. I am proposing work on three aspects of the analysis of complex survey data. The first topic is that of density estimation for the joint distribution of event times and for spatial analysis; the former is relevant when we are interested in assessing whether one type of event might trigger another, and the latter will be applicable to the visualization of spatial distributions of disease incidence. The second topic is estimation in multilevel models, wherein explanatory variables at more than one level of aggregation are used to model responses; since this kind of modeling is increasingly widely used, resampling methods which allow for the complex sampling design in estimating components of variance, and which can be made available to analysts, will be explored. Finally, I will study applications of hierarchical models to account for population heterogeneity in the analysis of survey data, with particular application to cross-cultural survey methods. This topic is of great interest to social scientists and health scientists in the context of international surveys.