A Bayesian h‐index: How to measure research impact. (3rd November 2016)

• Record Type: Journal Article
• Authors: Cerchiello, Paola; Giudici, Paolo
• Is Part Of: Statistical analysis and data mining. Volume 9:Number 6(2016)
• Page Start: 388
• Page End: 396
• Publication Date: 2016-11-03
• Subjects: Bayesian approach -- h index -- negative binomial distribution -- overdispersion; Data mining -- Statistical methods -- Periodicals; 006.312
• DOI: 10.1002/sam.11326
• Languages: English
• ISSNs: 1932-1864

Abstract

Abstract : The quality of academic research is difficult to measure and rather controversial. Hirsch has proposed the h index [1], a measure that has the advantage of summarizing in a single summary statistic the information that is contained in the citation counts of each scientist. Although the h index has received a great deal of interest, only a few papers have analyzed its statistical properties and implications. We claim that statistical modeling can give a lot of added value over a simple summary like the h index. To show this, in this paper we propose a negative binomial distribution to jointly model the two main components of the h index: the number of papers and their citations. We then propose a Bayesian model that allows to obtain posterior inferences on the parameters of the distribution and, in addition, a predictive distribution for the h index itself. Such a predictive distribution can be used to compare scientists on a fairer ground, and in terms of their future contribution, rather than on their past performance.