This work applies a factor analysis with VARIMAX rotation to develop a bibliometric indicator, named the Weighted Factor Index, in order to derive a new classification for journals belonging to a certain category, alternative to the one provided by the Journal Impact Factor. For this, 16 metrics from three different databases (Web of Science, Scopus and SCImago Journal Rank) are considered. The Weighed Factor Index entails the advantage of incorporating and summarizing information from all the indicators; so as to test its performance, it was applied to rank journals belonging to the category Information Science & Library Science.

The aim of this paper is the imputation of missing data of COVID-19 hospitalized and intensive care curves in several Spanish regions. Taking into account that the curves of cases, deceases and recovered people are completely observed, a function-on-function regression model is proposed to estimate the missing values of the functional responses associated with hospitalized and intensive care curves. The estimation of the functional coefficient model in terms of principal components’ regression with the completely observed data provides a prediction equation for the imputation of the unobserved data for the response. An application with data from the first wave of COVID-19 in Spain is developed after properly homogenizing, registering and smoothing the data in a common interval so that the observed curves become comparable. Finally, Canonical Correlation Analysis is performed on the functional principal components to interpret the relationship between hospital occupancy rate and illness response variables.