In recent years, statistical methods such as bibliometrics have increasingly intensified to analyse books, articles, and other publications. Bibliometric methods, as techniques to measure the information distribution models, are frequently used in the field of information science and social research. The main purpose of this article is to offer scholars a general framework for the comparison between positive and negative aspects of bibliometrics, on the methods and tools used. Therefore, both the strengths and the critical points will be highlighted, to obtain a complete and detailed overview of the entire argument. In the methodological part, a bibliometric analysis will be applied to various case studies, such as with the Generalized Error Distribution, analysing and commenting on the data, and using the Bibliometrix software. The results suggest that in the future there will be greater consolidation of bibliometrics, as the introduction of increasingly advanced technologies will create new tools and methods characterized by a high degree of automation and speed.
This paper develops a new time series clustering procedure allowing for heteroskedasticity, non-normality and model's non-linearity. At this aim, we follow a fuzzy approach. Specifically, considering a Dynamic Conditional Score (DCS) model, we propose to cluster time series according to their estimated conditional moments via the Autocorrelation-based fuzzy C-means (A-FCM) algorithm. The DCS parametric modelling is appealing because of its generality and computational feasibility. The usefulness of the proposed procedure is illustrated using an experiment with simulated data and several empirical applications with financial time series assuming both linear and nonlinear models' specification and under several assumptions about time series density function.
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