Random effects modeling of longitudinal and spatial data with and without zero-inflation.

有和没有零通货膨胀的纵向和空间数据的随机效应建模。

• 批准号: RGPIN-2017-04246
• 负责人: Hasan, Tariqul
• 金额: $ 1.02万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=758880
• 关键词: Random; effects; modeling; longitudinal; spatial

My proposed research is mainly focusing on random effect modeling of longitudinally, spatially and spatiotemporally correlated skewed data occurring in medical, health, ecological, environmental and biological studies. To be specific, we model data that are observed over time (time series), on many individuals over time (longitudinal), over space/location (e.g. spatial), over space and time (e.g. spatiotemporal). My research interest lies in the following three areas.First, I would continue working on random effect modeling of the longitudinal skewed data with particular interest in zero-inflated data. In longitudinal count data, responses collected over time frequently suffer the presence of excessive zero. In the literature, two-part model (also known as delta-gamma approach) is commonly used to model longitudinal skewed data, where the presence-absence and the positive responses are modeled separately. As this type of separate modeling of breaking between zero and non-zero values presents unnatural discontinuity in density function, it destroys the serial correlation structures commonly present in the longitudinal data. To overcome this problem, I propose to incorporate distribution-free random effects into the flexible class of Tweedie generalized linear models to model various types of zero-inflated longitudinal skewed responses. Second, I will extend methodologies proposed in the previous research area to spatial and spatiotemporal skewed datadata. My focus will be to develop the methodology to handle the data where response observed overtime at different spatial locations which are nested within other spatial locations for skewed continuous, semi-continuous and count data. I propose to develop Tweedie mixed models considering two levels of spatial random effects, which can also accommodate various flexible spatiotemporal correlation structures. Third, I will focus on joint modeling of mixed types of spatiotemporal data including count, continuous and semi-continuous data where responses are jointly collected along with various covariates for number of years from ecological districts which are nested within ecological regions. Failure to take appropriate account of this joint modeling phenomenon of the data set can lead to biased estimation of the regression parameters. Joint modeling has received increasing attention in the recent years due to the fact that it may increase statistical efficiency by using all of the data simultaneously in a single model. In the literature there are few approaches available to model longitudinal and survival data jointly. To the best of our knowledge there is nothing available to model spatiotemporal data jointly including count, continuous and semi-continuous data. I propose to develop a Tweedie mixed model based computationally simpler approach to jointly model count, continuous and semi-continuous data in an integral way.

我建议的研究主要集中在纵向，空间和时空相关的倾斜数据发生在医学，健康，生态，环境和生物研究的随机效应建模。具体来说，我们对随时间（时间序列）、随时间（纵向）、随空间/位置（例如空间）、随空间和时间（例如时空）观察到的数据进行建模。我的研究兴趣在于以下三个方面：第一，我将继续研究纵向偏斜数据的随机效应建模，特别是零膨胀数据。在纵向计数数据中，随着时间的推移收集的响应经常遭受过零的存在。在文献中，两部分模型（也称为delta-gamma方法）通常用于建模纵向偏斜数据，其中存在-不存在和正响应分别建模。由于这种类型的零值和非零值之间的分离建模在密度函数中呈现出不自然的不连续性，因此它破坏了纵向数据中通常存在的序列相关结构。为了克服这个问题，我建议将分布自由的随机效应到灵活的类Tweedie广义线性模型，以模拟各种类型的零膨胀的纵向偏斜响应。其次，我将在以前的研究领域提出的方法，空间和时空偏斜的数据。我的重点是开发处理数据的方法，其中在不同的空间位置随时间观察到响应，这些空间位置嵌套在其他空间位置中，用于倾斜的连续、半连续和计数数据。我建议开发Tweedie混合模型考虑两个层次的空间随机效应，它也可以容纳各种灵活的时空相关结构。第三，我将专注于联合建模的混合类型的时空数据，包括计数，连续和半连续的数据，响应联合收集沿着与各种协变量的数量，从生态区嵌套在生态区域。如果不适当考虑数据集的这种联合建模现象，可能会导致回归参数的有偏估计。联合建模由于可以在一个模型中同时使用所有数据来提高统计效率，近年来受到越来越多的关注。在文献中，很少有方法可用于联合建模纵向和生存数据。据我们所知，没有什么可用于联合建模时空数据，包括计数，连续和半连续数据。我建议开发一个Tweedie混合模型为基础的计算更简单的方法，联合模型计数，连续和半连续的数据在一个整体的方式。