id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-153150-rep51ly9	Tang, Chen	Functional data analysis: An application to COVID-19 data in the United States	2020-09-17		.txt	text/plain	7805	457	61	We explore the modes of variation of the data through a functional principal component analysis (FPCA), and study the canonical correlation between confirmed and death cases. Lastly, we consider a functional time series model fitted to the cumulative confirmed cases in the US, and make forecasts based on the dynamic FPCA. To begin with, we plot the fitted mean curve (which estimates the trend over time), the fitted variance curve (which estimates the subject-specific variation) and the fitted covariance surface of daily confirmed cases across 50 continental states in Figure 3 . Prior to estimating the functional canonical correlation between confirmed cases and death tolls in the US, some additional pre-processing procedures to the data are necessary, as we observe that the date on which the first confirmed case is reported varies significantly across the states, and the number of death counts stays relatively low during the entire study period in several states.	./cache/cord-153150-rep51ly9.txt	./txt/cord-153150-rep51ly9.txt