Inference and computational methods for mixed models with large or complex data

混合效应模型在统计学的许多应用领域都很有用。通过考虑实验单元内的相关性，它们产生的推论和预测比没有利用数据中的这种结构的方法更准确和有效。它们还可以保护估计不受不可观测变量引起的偏差的影响。这些特性使它们对许多现代用途具有吸引力。例如，它们可以用来从GPS项圈产生的数据推断动物运动的决定因素，计算考虑到汽车传感器收集的几乎连续时间数据的保险费，使用行政健康数据库开发个性化的医疗策略，或者理解社交媒体中的“事件”之间的关联。尽管混合效应模型已经被研究了几十年，但它们仍然是许多正在进行的研究的重点，因为需要进一步的发展才能使它们更适合上述类型的现代任务。事实上，作为我之前的NSERC Discovery Grant的一部分，我们开发了一种两步法来适应复杂响应依赖的二进制数据的混合模型，当数据由中等数量的非常大的簇组成时，这种方法是非常高效的。 在接下来的几年里，我的研究计划打算在这些最新发展的基础上再接再厉，包括开发新的工具，以适应大型数据集和/或通过响应依赖抽样方案获得的数据集。该研究项目在多个方面进行了创新。短期内，将推导出在多核机器上易于使用和计算的型号选择标准，并将其添加到我们的R包TwoStepCLogit中。这将使生物、生态和环境科学领域的众多最终用户能够使用新方法，使用他们不断增长的记录动物运动数据的地理信息系统/全球定位系统数据库。在中期内，我将调整我们的两步法，使其适用于大规模数据库，其中大量客户或患者被纵向跟踪(例如，保险、营销、药物流行病学、Twitter和社交媒体数据)。行业合作伙伴可能会在这个阶段参与进来，并应该为学生/博士后提供数据和实习机会。这种新方法的优点是它很容易适应高度并行的计算。从长远来看，我们将努力利用两步法是基于EM算法的事实，以推导出这些方法的在线实现(即，一旦有新的数据点进入，就更新模型FIT)。同样，实现这些方法的R包将公开可用。

Mixed effects models are useful in many fields of application of statistics. By taking the within-experimental-unit correlation into account, they yield inferences and predictions that are more accurate and efficient than methods that do not exploit this structure within the data. They can also protect the estimations against bias induced by unobserved variables. These properties make them attractive for many modern uses. For instance, they can be used to infer on the determinants of animal movement from data generated by GPS collars, to compute insurance premiums that take into account the near continuous-time data gathered by automobile sensors, to develop personalized medicine strategies using administrative health databases or to understand the associations between "events" in social media. Even though mixed effects models have been investigated for decades, they are still the focus of much ongoing research because further developments are required to make them better suited for modern tasks of the type described above. Indeed as part of my previous NSERC Discovery Grant we developped a Two-Step method to fit mixed models to complex response dependent binary data that is highly efficient when the data consist of a moderate number of very large clusters. My research program over the next few years intends to build upon these recent developments and consists in developing new tools to fit mixed effects models to large datasets and/or to datasets obtained through response dependent sampling schemes. This research program innovates in many aspects. In the short term, model selection criteria that are easy to use and compute on multi-core machines will be derived and added to our R package TwoStepCLogit. This will enable the numerous end users in biology, ecology and environmental sciences to use the new methods with their ever growing GIS/GPS databases that record animal movement data. In the medium term, I will adapt our Two-Step method so that it can fit generalized mixed regression models to massive databases where a large number of clients or patients are followed longitudinally (e.g., insurance, marketing, pharmacoepidemiology, twitter and social media data). Industrial partners will likely get involved at this stage and should provide data and internship opportunities for students/postdocs. The advantage of this new method is that it will easily be amenable to highly parallelized computing. In the longer term, we will try to capitalize on the fact that the Two-Step method is based on the EM-algorithm to derive an on-line implementation of the methods (i.e., update the model fit as soon as a new data point comes in). Again, R packages to implement the methods will be made publicly available.