The Impacts of Bibliometrics Measurement in the Scientific Community A Statistical Analysis of Multiple Case Studies

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

Improving Volatility Forecasts with GED-GARCH Model: Evidence from U.S. Stock Market

Recently, great strides have been made in predicting volatility in the financial market. However, the so widely used GARCH model suffers from several problems, like the normality assumption which can lead to unreliable estimates and forecasts. In order to solve this problem, it is possible to introduce different new assumptions in the classical GARCH framework. In particular, the GED-GARCH model supposes that the innovation distribution follows a Generalized Error Distribution (GED). In this paper, after a brief theoretical discussion of the statistical model, we show empirically that it is possible to obtain better results using the GED-GARCH(1,1) instead of GARCH(1,1) based on the normality or t-student assumptions

Bonferroni-Holm and permutation tests to compare health data: methodological and applicative issues

Abstract Background Statistical methodology is a powerful tool in the health research; however, there is wide accord that statistical methodologies are not usually used properly. In particular when multiple comparisons are needed, it is necessary to check the rate of false positive results and the potential inflation of type I errors. In this case, permutation testing methods are useful to check the simultaneous significance level and identify the most significant factors. Methods In this paper an application of permutation tests, in the medical context of Inflammatory Bowel Diseases, is performed. The main goal is to assess the existence of significant differences between Crohn’s Disease (CD) and Ulcerative Colitis (UC). The Sequentially Rejective Multiple Test (Bonferroni-Holm procedure) is used to find which of the partial tests are effectively significant and solve the problem of the multiplicity control. Results Applying Non-Parametric Combination (NPC) Test for partial and combined tests we conclude that Crohn’s Disease patients and Ulcerative Colitis patients differ between them for most examined variables. UC patients compared with the CD patients, have a higher diagnosis age, not show smoking status, proportion of patients treated with immunosuppressants or with biological drugs is lower than the CD patients, even if the duration of such therapies is longer. CD patients have a higher rate of re-hospitalization. Diabetes is more present in the sub-population of UC patients. Analyzing the Charlson score we can highlight that UC patients have a more severe clinical situation than CD patients. Finally, CD patients are more frequently subject to surgery compared to UC. Appling of the Bonferroni Holm procedure, which provided adjusted p-values, we note that only nine of the examined variables are statistically significant: Smoking habit, Immunosuppressive therapy, Surgery, Biological Drug, Diabetes, Adverse Events, Re-hospitalization, Gender and Duration of Immunosoppressive Therapy. Therefore, we can conclude that these are the specific variables that can discriminate effectively the Crohn’s Disease and Ulcerative Colitis groups. Conclusions We identified significant variables that discriminate the two groups, satisfying the multiplicity problem, in fact we can affirm that Smoking habit, Immunosuppressive therapy, Surgery, Biological Drug, Diabetes, Adverse Events, Hospitalization, Gender and Duration of Immunosoppressive Therapy are the effectively significant variables