Abstract
AbstractCountries’ performance can be compared by means of indicators, which in turn give rise to rankings at a given time. However, the ranking does not show whether a country is improving, worsening or is stable in its performance. Meanwhile, the evolutionary behaviour of a country’s performance is of fundamental importance to assess the effect of the adopted policies in both absolute and comparative terms. Nevertheless, establishing a general ranking among countries over time is an open problem in the literature. Consequently, this paper aims to analyze ranks’ dynamic by means of the functional data analysis approach. Specifically, countries’ performances are evaluated by taking into account both their ranking position and their evolutionary behaviour, and by considering two functional measures: the modified hypograph index and the weighted integrated first derivative. The latter are scalar measures that are able to reflect trajectories behaviours over time. Furthermore, a novel visualisation technique based on the suggested measures is proposed to identify groups of countries according to their performance. The effectiveness of the proposed method is shown through a simulation study. The procedure is also applied on a real dataset that is drawn from the Government Effectiveness index of 27 European countries.
Funder
Università degli Studi Roma Tre
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability
Reference51 articles.
1. Aguilera A, Fortuna F, Escabias M, Di Battista T (2021) Assessing social interest in burnout using google trends data. Soc Indic Res 156:587–599
2. Babu G, Canty A, Chaubey Y (2002) Application of Bernstein polynomials for smooth estimation of a distribution and density function. J Stat Plan Inference 105:377–392
3. Bellantuono L, Monaco A, Tangaro S, Amoroso S, Aquaro V, Bellotti R (2020) An equity-oriented rethink of global rankings with complex networks mapping development. Nat Sci Rep 10:18046
4. Bibi N, Shah I, Alsubie A, Ali S, Lone S (2021) Electricity spot prices forecasting based on ensemble learning. IEEE Access 9:150984–150992
5. Bongiorno E, Goia A (2019) Describing the concentration of income populations by functional principal component analysis on lorenz curves. J Multivar Anal 170:10–24
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献