Mariano J. Valderrama

The literature on the JIF is abundant due to its frequent use as an indicator in evaluation processes. In this collection of publications referring to it, observations about its limitations or inadequacy for the purposes to which it is applied are perhaps the most frequent section. One of the reasons for this criticism is the concentration of citations in a small fraction of the articles, which, according to the authors of these observations, undermines the value of the indicator insofar as it applies a measure based on averages to distributions that are highly skewed by this concentration of citations. This study analyses whether the JIF is indeed as affected by this distortion as has been pointed out, which would reduce its value as an indicator. Alternatively, the effect of Highly Cited Papers (HCP) may not disrupt the overall structure of journals in their respective categories when ranked according to this criterion. In this case, the value of this indicator, without going into other limitations that may affect it, would remain valid from this point of view in its application in evaluation exercises and, with it, other associated indicators such as quartiles. The study also analyses the behaviour of HCPs and whether their weight in the total journal citations has such a significant effect that, beyond a certain threshold, it would advise against using JIFs for this reason.

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.

The problem of developing a 2-week-on ahead forecast of atmospheric cypress pollen levels is tackled in this paper by developing a principal component multiple regression model involving several climatic variables. The efficacy of the proposed model is validated by means of an application to real data of Cupressaceae pollen concentration in the city of Granada (southeast of Spain). The model was applied to data from 11 consecutive years (1995–2005), with 2006 being used to validate the forecasts. Based on the work of different authors, factors as temperature, humidity, hours of sun and wind speed were incorporated in the model. This methodology explains approximately 75–80% of the variability in the airborne Cupressaceae pollen concentration.