Translation-invariant functional clustering on COVID-19 deaths adjusted on population risk factors
针对欧洲和美国COVID-19死亡率数据，提出一种三步聚类方法，通过平移不变小波分解处理疾病到达时间差异，并用单指标回归消除人口风险因素影响，最后对残差进行非参数混合聚类。
Abstract This paper focuses on clustering the COVID-19 death rates reported in Europe and the United States. Several methods have been developed to cluster such functional data. However, these methods are not translation-invariant (TI) and thus cannot handle different times of arrivals of the disease, nor can they consider external covariates and so are unable to adjust for the population risk factors of each region. We propose a novel three steps clustering method to circumvent these issues. First, feature extraction is performed by TI wavelet decomposition, which permits to deal with the different onsets. Then, single-index regression is used to neutralize disparities caused by population risk factors. Finally, a nonparametric mixture is fitted on the regression residuals to achieve the region clustering.