"Local-EM, spatial-temporal modelling and mismeasured data"

不完整的数据可能是区间删失的，可能是在小组研究中出现的，也可能是时空性质的。例如，研究罕见疾病的环境决定因素可能涉及几十年来定期收集的人口普查数据，并跨越广阔的地理区域。这些数据在空间和时间上都是汇总的，而且由于空间汇总是随时间变化的，因为人口普查的边界会改变，以反映城市增长及其对农村地区的影响，因此可能会使情况复杂化。例子包括大多伦多地区狼疮的研究和安大略省兰顿和米德尔塞克斯县间皮瘤的发病率。 该项目旨在为这种不完整的数据开发时空方法。这些方法将是灵活的和计算密集型的。中央设备是一个本地的可能性，其使用的背景下，不完整的数据自然导致本地EM算法的发展。Fan，斯塔福德和布朗（2011）通过暴露其与EMS算法的关系，从而将这类算法嵌入到更广泛的上下文中，从而形成更大类的惩罚似然性。这样做的优点，这是要探讨的，是它允许的途径，研究本地EM算法和由此产生的估计。这包括理论和推理特性的发展，流行病学研究中常见的时空背景下的方法的扩展，并纳入误测数据。后者将把至少一些这样的研究的背景下，在界面上的结构模型的缺失数据和功能模型的测量误差观察到的数据。最后，要开发的方法是计算密集型的，但可以使用多个处理器来实现。因此，他们利用并行计算的现代趋势，大大提高了计算能力。通过具有服从并行计算的优点，该方法优于替代MCMC实现。计算将在SciNet HPC Consortium的通用集群超级计算机上进行。

Data that are incomplete may be interval censored, may have arisen in a panel study or may be spatial-temporal in nature. For example, studying the environmental determinants of a rare disease may involve census data collected periodically over decades and across an expansive geographic region. Such data is aggregated in both space and time, and can be complicated by the fact that the spatial aggregation is time dependent as census boundaries change to reflect urban growth and its impact on rural regions. Examples includes a study of Lupus in the Greater Toronto Area and the incidence of mesothelioma in Lambton and Middlesex counties in the Province of Ontario. This project aims to develop spatial-temporal methods for such incomplete data. The methods will be flexible and computationally intensive. The central device is a local likelihood whose use in the context of incomplete data leads naturally to the development of local-EM algorithms. Fan, Stafford and Brown (2011) embed this class of algorithms in a broader context by exposing its relationship to the EMS algorithm and hence a larger class of penalized likelihoods. The advantages of this, which are to be explored, is that it permits avenues of study for local-EM algorithms and resulting estimators. This includes the development of theoretic and inferential properties, the extension of methods to spatial-temporal contexts common in epidemiological studies, and the incorporation of mismeasured data. The latter will place at least some of this research in a context that lies at the interface of structural models for missing data and functional models for data observed with measurement error. Finally, the methods to be developed are computationally intensive but can implemented using multiple processors. Consequently they exploit modern trends in parallel computing that are dramatically increasing computational power. By having the advantage of being amenable to parallel computing the methods outperform alternative MCMC implementations. Computations will be performed on the General Purpose Cluster supercomputer at the SciNet HPC Consortium.