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A Bayesian h‐index: How to measure research impact
Author(s) -
Cerchiello Paola,
Giudici Paolo
Publication year - 2016
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11326
Subject(s) - measure (data warehouse) , bayesian probability , index (typography) , negative binomial distribution , statistic , computer science , statistics , binomial distribution , bayesian inference , posterior probability , data mining , econometrics , mathematics , artificial intelligence , world wide web , poisson distribution
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.