Statistical inference on the h-index with an application to top-scientist performance

TL;DR

This paper develops statistical inference methods for the h-index, enabling more rigorous evaluation of top scientists' performance through point and set estimation, and applies these techniques to Nobel Laureates and Fields medallists.

Contribution

It introduces new statistical inference procedures for the h-index, including point and set estimation, and addresses simultaneous inference for scholar comparisons.

Findings

Effective point and set estimation methods for the h-index.

Application to Nobel Laureates and Fields medallists datasets.

Enhanced comparison techniques for top scientists.

Abstract

Despite the huge amount of literature on h-index, few papers have been devoted to the statistical analysis of h-index when a probabilistic distribution is assumed for citation counts. The present contribution relies on showing the available inferential techniques, by providing the details for proper point and set estimation of the theoretical h-index. Moreover, some issues on simultaneous inference - aimed to produce suitable scholar comparisons - are carried out. Finally, the analysis of the citation dataset for the Nobel Laureates (in the last five years) and for the Fields medallists (from 2002 onward) is proposed.