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Functional Principal Component Analysis (FPCA) is an important dimension reduction technique to interpret themainmodes of functional data variation in terms of a small set of uncorrelated variables. The principal components can not always be simply interpreted and rotation is one of the main solutions to improve the interpretation. In this paper, two new functional Varimax rotation approaches are introduced. They are based on the equivalence between FPCA of basis expansion of the sample curves and Principal Component Analysis (PCA) of a transformation of thematrix of basis coefficients. The first approach consists of a rotation of the eigenvectors that preserves the orthogonality between the eigenfunctions but the rotated principal component scores are not uncorrelated. The second approach is based on rotation of the loadings of the standardized principal component scores that provides uncorrelated rotated scores but non-orthogonal eigenfunctions. A simulation study and an application with data from the curves of infections by COVID-19 pandemic in Spain are developed to study the performance of these methods by comparing the results with other existing approaches. ; Spanish Ministry of Science, Innovation and Universities (FEDER program) MTM2017-88708-P ; Government of Andalusia (Spain) FQM-307 FPU18/01779

The authors would like to thank Rodrigo Costas and Antonio Callaba de Roa for their helpful comments in previous version of this paper as well as the two anonymous reviewers for the constructive comments. We would also like to thank Bryan J. Robinson for revising the text. Nicolas Robinson-García is currently supported with a FPU grant from the Spanish government, Ministerio de Economía y Competitividad. ; Background: The peer review system has been traditionally challenged due to its many limitations especially for allocating funding. Bibliometric indicators may well present themselves as a complement. Objective: We analyze the relationship between peers' ratings and bibliometric indicators for Spanish researchers in the 2007 National R&D Plan for 23 research fields. Methods and materials: We analyze peers' ratings for 2333 applications. We also gathered principal investigators' research output and impact and studied the differences between accepted and rejected applications. We used the Web of Science database and focused on the 2002-2006 period. First, we analyzed the distribution of granted and rejected proposals considering a given set of bibliometric indicators to test if there are significant differences. Then, we applied a multiple logistic regression analysis to determine if bibliometric indicators can explain by themselves the concession of grant proposals. Results: 63.4% of the applications were funded. Bibliometric indicators for accepted proposals showed a better previous performance than for those rejected; however the correlation between peer review and bibliometric indicators is very heterogeneous among most areas. The logistic regression analysis showed that the main bibliometric indicators that explain the granting of research proposals in most cases are the output (number of published articles) and the number of papers published in journals that belong to the first quartile ranking of the Journal Citations Report. Discussion: Bibliometric indicators predict the concession of grant proposals at least as well as peer ratings. Social Sciences and Education are the only areas where no relation was found, although this may be due to the limitations of the Web of Science's coverage. These findings encourage the use of bibliometric indicators as a complement to peer review in most of the analyzed areas

Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s11192- 021- 04195-4. ; Although this phrase is usually written in the articles as a courtesy, in our case we sincerely thank two anonymous reviewers for the interest and time they have dedicated to studying our work, since their suggestions have contributed decisively to improve its final version. ; Funding for open access charge: Universidad de Granada / CBUA. This research was funded in part by project PID2020-113961GB-I00 of the Spanish Ministry of Science and Innovation, project A-FQM-66-UGR20 of Vicerectorate for Research and Transfer of the University of Granada (also supported by the FEDER program), and project FQM-307 of the Government of Andalusia (Spain). ; 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. ; Universidad de Granada/CBUA ; Project PID2020-113961GB-I00 of the Spanish Ministry of Science and Innovation ; Project A-FQM-66-UGR20 of Vicerectorate for Research and Transfer of the University of Granada (also supported by the FEDER program) ; FQM-307 of the Government of Andalusia (Spain)

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. ; MTM2017-88708-P of the Spanish Ministry of Science, Innovation and Universities (also supported by the FEDER program) ; FQM-307 of the Government of Andalusia (Spain) ; Ph.D. grant (FPU18/01779)