Generalised functional additive mixed models with compositional covariates for areal Covid-19 incidence curves

Clustering Areal Units at Multiple Levels of Resolution to Model Crime in Philadelphia

Estimation of the spatial heterogeneity in crime incidence across an entire city is an important step towards reducing crime and increasing our understanding of the physical and social functioning of urban environments. This is a difficult modeling endeavor since crime incidence can vary smoothly across space and time but there also exist physical and social barriers that result in discontinuities in crime rates between different regions within a city. A further difficulty is that there are different levels of resolution that can be used for defining regions of a city in order to analyze crime…

空间时间自回归模型用于区域组成数据

Spatiotemporal Autoregressive Models for Areal Compositional Data

本文提出一种空间时间多变量自回归模型，用于分析区域层面的组成数据，通过准最大似然估计方法联合建模时间动态和空间依赖性，应用于柏林住房市场和西班牙经济领域。

Analyzing Cause-Specific Mortality Trends using Compositional Functional Data Analysis

We study the dynamics of cause--specific mortality rates among countries by considering them as compositions of functions. We develop a novel framework for such data structure, with particular attention to functional PCA. The application of this method to a subset of the WHO mortality database reveals the main modes of variation of cause--specific rates over years for men and women and enables us to perform clustering in the projected subspace. The results give many insights of the ongoing trends, only partially explained by past literature, that the considered countries are undergoing. We are…